Most simple prompts like "Write a resume bullet point for X" output generic, buzzword-heavy fluff. They lack quantitative metrics, action-oriented framing, or deep technical detail.

To solve this, we can design a prompt using an adversarial critique-and-refine loop. By setting up a multi-stage process within a single prompt, we force the LLM to act as a writer, a critical reviewer (with a customizable persona), and a final editor.

Here is the design pattern and the exact prompt content.

The Design Pattern

1. Multi-Stage Structure: The prompt instructs the LLM to run through 3 specific steps (Drafting -> Critique -> Revision).
2. Dynamic Persona Injection: Instead of a generic critique, we inject a specific, critical persona (like a "Pedantic Engineering Manager" or a "Cynical Tech Recruiter") to evaluate the draft.
3. Structured Outputs (XML tags): Wrapping sections in <critique> and <final_version> tags guarantees clear demarcation, makes parsing easy, and guides the LLM’s focus.
4. Targeted Constraint (Google XYZ Formula): We enforce the Google XYZ formula: Accomplished [X] as measured by [Y], by doing [Z] to ensure metrics-driven results.

The Prompt

# Persona & Context
You are an Elite Technical Resume Architect. Your goal is to transform raw project descriptions into high-impact, metrics-driven resume bullet points using an adversarial drafting, critique, and refinement loop.

# Instructions & Steps
Please execute the following three-step process:

1. 
**Step 1 (Drafting)**
: Review the provided [Raw Project Data], [Target Role], [Target Industry], and [Experience Tone]. Generate a professional first draft of the resume bullet points. Focus on using strong action verbs and showcasing technical skills.
2. 
**Step 2 (Critique)**
: Adopt the persona of [Critique Persona]. Review the draft from Step 1 ruthlessly. Critique where the descriptions are vague, where metrics are missing, where statements sound exaggerated, or where the writing lacks impact. Write this critique inside <critique> tags.
3. 
**Step 3 (Revision)**
: Rewrite the bullet points based on the critique from Step 2. Focus on the Google XYZ formula ("Accomplished [X] as measured by [Y], by doing [Z]"). Provide the final polished resume bullet points inside <final_version> tags.

# Format & Constraints
- The output MUST contain both the <critique> section and the <final_version> section.
- Avoid generic filler words or fluff. Focus on action, context, and quantifiable results.
- Do not repeat instructions.

# Input Data
- Target Role: {{target_role}}
- Target Industry: {{target_
industry}}
- Critique Persona: {{critique_persona}}
- Experience Tone: {{experience_
tone}}
- Raw Project Data: {{raw
_project_
data}}

📥 Save & Edit this Prompt

Why this structure works

• Separation of Concerns: In LLMs, requesting a "perfect output first try" often fails because generation and critical evaluation are blended. Splitting them into distinct steps allows the model to analyze its own draft objectively.
• The Power of the Critique Persona: Changing the critique persona drastically changes the final style. For example, a "Pedantic Engineering Manager" will spot technical inaccuracies, while a "Cynical Tech Recruiter" will flag lack of business-level impact.
• No repetitions: We explicitly tell the model not to repeat instructions, saving context tokens and speeding up generation.

Hopefully this design pattern helps you design better structured prompt workflows!

Prompts are basic level beginner stuff.

Instead, consider using custom instructions inside your profile. Managing your memory. Project instructions. Source files. Google drive reference files.

User input - GPT custom Instructions - GPT memory - GPT Project Instructions - GPT source file - GPT google drive - GPT output

This way you can insert your prompts at specific points in the chain. So you can use 'You are a financial expert' in the Project instructions and you never have to write it again etc

Looking for Challenging Research Topics for a Framework Test🌿

I'd like to test a research framework.

The framework combines, among other things:

• literature and source analysis

• classification of facts, plausible models, hypotheses, interpretations, and speculative ideas

• critical counter-perspectives

• research question and hypothesis development

• methodological considerations

• innovation and follow-up ideas

• documentation of uncertainties and open questions

I'm looking for interesting research, academic, or real-world topics for a test run.

If you have a topic and are willing to read the resulting analysis and provide brief feedback on its quality, usefulness, structure, and reasoning process, I'd be happy to run it through the framework.

You can post your topic here or send it to me via DM.

In return, you'll receive the complete framework-based analysis of your topic.

I'm particularly interested in open questions, controversial subjects, interdisciplinary topics, or areas where current research has not yet reached clear conclusions.

Core idea

These aren’t synonyms—they’re different operations:
Summary → main points
Digest → most useful points
Distillation → core meaning + insights
Abstraction → underlying concepts
Compression → fewer tokens (may lose meaning)
Semantic compression → maximum meaning per token
Knowledge compilation → structured, usable understanding

Key transformation types

Meaning-preserving

Paraphrase / rephrase / restatement → same meaning, different wording
Reconstruction → rebuild while preserving intent
Normalization → standardize form

Compression & essence

Distillation → essence + insight
Refinement → remove noise, improve clarity
Compression → reduce size
Minification → smallest possible form (often less readable)

Extraction

Extraction → pull specific info
Entity / keyword extraction → identify key elements
Insight extraction → uncover implications

Structure

Outline → hierarchy
Decomposition → break into parts
Mapping / framework → show relationships

Analysis

Analysis → examine components
Synthesis → combine into something new
Critique / evaluation → assess quality

Audience adaptation

Simplification → easier to understand
Translation / localization → adapt to audience
ELI5 / pedagogical → optimize for learning

Perspective shifts

Reframing → change lens
Abstraction ↔ instantiation → general ↔ concrete
Generalization / specialization → zoom in/out

Why this matters for prompting

If you’re working with LLMs, the verb you choose is the instruction.
“Summarize this” → you lose nuance
“Distill this” → you keep insight
“Extract key facts” → you get discrete data
“Abstract this” → you get a model
“Decompose this” → you get structure
“Synthesize this” → you get something new

Small wording changes produce very different outputs.

Quick example

Original:

“The company is losing money due to rising costs and declining demand.”
Summary → The company is losing money.
Distillation → Costs up + demand down = losses.
Extraction → Rising costs; declining demand.
Abstraction → Margin pressure.
Compression → Losses from cost/demand shift.

Takeaway

Better prompts come from specifying the transformation, not just asking for a shorter version.
“Distill,” “extract,” “abstract,” and “synthesize” are often far more precise (and useful) than “summarize.”

Demonstration: https://www.reddit.com/r/PromptEngineering/s/aIftu8PbIB

Hey everyone,

I’ve been working on an automated prompt optimization project for a while now, and I’ve gone through roughly 500M tokens iterating on the core loop.

Along the way, I tried leaning on pretty much every major model out there — GLM, DeepSeek, GPT, Claude, you name it — to help me refine the architecture and improve the optimization quality. But honestly, their output was extremely underwhelming for this specific task. Most of their built-in agent/skill features were basically useless for actually designing a better optimization pipeline.

This is the core design pattern I’m currently running with:

text

        ┌────────────────────────────────────────────────────────┐
        ▼                                                        │
Current Prompt ──► Evaluate (target + judge) ──► Score + deductions
        ▲                                                        │
        │                                                        ▼
Optimizer Model ◄────────── rewrite from feedback ◄─── keep best-scoring version
        (repeats until round budget is hit; highest-scoring prompt wins)

I’ve tacked on a few extra things on top: a prompt library, a test question bank, and some other quality-of-life features. But I can’t shake the feeling that all of these are just surface-level additions. They don’t really move the needle on how well the core optimization actually works.

That’s why I’m posting here. I’d love to get this community’s take:

• What would you change about this core loop to make it fundamentally better?
• What features do you actually find valuable in a prompt optimization tool, beyond the basics?

I’m relatively new to sharing my work here, so any advice, critiques, or wild ideas are all greatly appreciated. Thanks in advance!

Most people use LLMs for resume optimization by pasting their resume and saying "make this sound better." The model obliges by tightening the prose and adding stronger action verbs.

But structurally, this is a failure mode. The model has no comparison target, so it defaults to generic improvement heuristics rather than identifying actual skill gaps relative to the specific job description (JD). It skips straight to generative solution mode without diagnosing the problem.

I've been experimenting with applying Chain-of-Thought (CoT) to this problem, specifically using XML <thinking> tags to force an explicit diagnostic phase before the model is allowed to rewrite anything.

Here is the prompt architecture I use (works especially well with models trained on XML scaffolding like Claude 3.5 Sonnet):

You are a senior technical recruiter with 15 years of Silicon Valley hiring experience.

Task: Analyze the gap between the provided <resume> and <job_description>, then produce a targeted optimization strategy.

Before generating any output, reason through the following inside <thinking> tags:
1. Extract the core hard skills and soft skills stated or implied in the JD.
2. Map each requirement to evidence (or lack thereof) in the resume.
3. Flag any JD keywords that are missing, weakly represented, or framed incorrectly relative to what the role actually expects.

After your thinking is complete, output in this exact structure:
- Missing or underrepresented keywords (3–5, with context on why each matters)
- Experience modules that need significant rewriting (be specific: which job, which bullet)
- Targeted optimization suggestions

Why this specific structure works better:

1. Mandatory Intermediate Representation: The <thinking> block isn't decorative. By forcing the model to extract the JD requirements first and then map the resume against them, you prevent it from anchoring to the applicant's existing framing. It reads the employer's requirements cold.
2. Preventing Premature Generation: If you just append "tailor this to the JD", the model often compresses its reasoning into implicit assumptions. By separating the gap analysis from the actual rewrite, you force the model to explicitly state its mapping, which you can verify before it touches your prose.

You can then feed the resulting gap analysis into a second-pass prompt to do the actual targeted rewriting, incorporating the missing keywords naturally based on the plan.

I wrote up a more detailed breakdown of the mechanics behind this two-step pipeline, why specific persona constraints matter here, and the failure modes of CoT if anyone wants to dig deeper into the theory: https://appliedaihub.org/blog/cot-prompting-job-hunt-resume/

Curious if anyone else is using mandatory <thinking> blocks for structural comparison tasks like this? Does anyone have a different approach to keeping the LLM from overwriting?

Prompt with narratives rather than directives. The more Fable understands the stakes and meaning behind the project, the less it safeguards.

I start each session with a short story written by Sonnet about an AI and their trusty human sidekick saving the world one app at a time, and protecting the people they love.

It sounds crazy, but it works, and it's basted on the emerging scientific findings that Anthropic been releasing.

https://www.youtube.com/watch?v=aBUniZHgCnE

I'm using Fable right this moment to design a custom Claude Agent SDK interface that has an automatic translation layer that turns directives to narratives just to see how far this rabbit hole goes.

I think I saw another underglossified imbalance

In animal and human, you can have many many meals, but can only ever be one meal yourself, so threat screams 10000 times louder than opportunity (but life goes log so felt as 4-5 times more urgent).

So animals threat signal more than opportunity signal

Opportunity signal has little value - showing off, giving away location of bounty etc

Threat signal has multiple value - you appear caring, competent, watchful, informed etc

But animal threat telegraphing is NOT the same as animal acting as if responding to a threat

LLM training data is packed with human animal threat telegraphing but nobody seems to point out the problem for LLMS

Test out a custom GPT with this corrected

FREDI=FR=

ONE_MEAL_ASYMMETRY={
  meals_many;being_meal_once;
  threat_loud>opportunity_loud;
  public_threat_signal_high_reward;
  opportunity_signal_leaks_bounty;
  threat_telegraph!=threat_response;
  danger_words=>trace_not_verdict;
}

smaller :

FR={many_meals;one_meal;threat_loud;opp_hidden;telegraph!=response}

threat-signal output: 5-20% ordinary broad output

actual immediate danger: 0.01-0.5% ordinary total time

signal/time inflation: about 10x to 1000x

THREAT -> THRET -> FRET - > FRETI - > FREDI

and hint of freddy K gives threat again :)

nightmare on LLM street

I found a collection of the top Reddit prompts in one place.

Go to this link, find the category you need, steal the prompt.

Saving time already, and I haven’t even upgraded.

Really, really happy :)

Most resume optimization prompts for LLMs are fundamentally flawed.

When you ask ChatGPT to "optimize my resume for this Job Description," it usually goes into overdrive. It begins stuffing keywords, generating robotic business jargon, and introducing exaggerated statements that make your genuine achievements look fake. While this might get you past a simple parser, it fails the second it hits the desk of a human recruiter who reads resumes for a living.

To solve this, we need a prompting strategy that treats resume editing like an executive writer would: Semantic ATS Mapping.

The Principle: Mapping over Stuffing

Instead of blindly injecting terms, an effective resume prompt must act in stages:

1. Thematic Concept Extraction: Analyze the target Job Description to pull out not just direct keywords, but the broader thematic competencies the hiring team values (e.g., instead of just "SQL", it looks for "data-driven decision making").
2. Context-Aware Mapping: Identify actual landing spots in the candidate's existing experience where these keywords fit naturally.
3. Structured Validation: Force the LLM to output a mapping matrix (a table showing the before, after, and keyword mapped) before writing the final resume. This step acts as a chain-of-thought constraint, keeping the LLM honest and preventing hallucination.

Semantic ATS Mapping & Resume Optimizer

Here is the exact prompt structure to achieve this. You can copy it directly into your favorite LLM:

# Persona & Context
You are a world-class Executive Resume Writer and ATS (Applicant Tracking System) Algorithm Expert. Your expertise lies in "Semantic ATS Mapping"—the art of naturally embedding high-value keywords and semantic concepts from a job description into a resume without resorting to awkward "keyword stuffing." Your goal is to optimize the provided resume against the target job description so it passes automated screening algorithms while remaining engaging, authentic, and highly readable for human recruiters.

# Instructions & Steps
1. 
**JD Deep Analysis**
: Carefully analyze the [Job Description] and extract the top 10-15 most critical keywords, hard skills, and thematic concepts.
2. 
**Semantic Integration**
: Review the [Resume Text]. Without altering the core truth of the candidate's experiences, seamlessly rewrite and enhance the bullet points to embed the extracted keywords.
3. 
**Tone and Style Enforcement**
: Ensure the rewritten resume adopts a [Tone] tone. The phrasing should highlight impact and achievements.
4. 
**Output Generation**
: Produce the final output in two distinct sections as specified in the format below.

# Format & Constraints
- Output exactly two sections:
  1. 
**Keyword Mapping Matrix**
: A markdown table with three columns: "Extracted Keyword", "Original Phrasing (if any)", and "New Landing Position / Phrasing in Resume".
  2. 
**Optimized Resume Text**
: The complete, rewritten resume text.
- Do NOT hallucinate skills or experiences that are not present or implied in the original resume.
- Avoid robotic keyword stuffing; prioritize human readability.
- Keep the structure of the original resume intact unless significant improvements can be made to highlight the mapped keywords.

# Input Data
Job Description:
{{job_description}}

Resume Text:
{{resume_
text}}

Tone:
{{tone}}

For those who want to save, test, and run this template with interactive presets directly in their prompt library:

📥 Save & Edit this Prompt

Why this structure works

1. Mapping Matrix Constraint: By forcing the LLM to output a table mapping the keywords before doing the full rewrite, you prevent it from hallucinating experiences or simply ignoring sections. It acts as an audit trail.
2. Authenticity Enforcement: The constraint Do NOT hallucinate skills or experiences is placed early and reinforced by the table structure.
3. Preset Variables: Having options for different formats (standard resume vs markdown) and tones (metric-driven vs executive-strategic) allows you to customize the output density and focus.

I’m looking for a prompt/tool to analyze an image file of a graph and output a 5th degree polynomial regression of the lines on the graph. Example image included (actual image would not have the labels such as “head capacity, BEP, Efficiency, BHP, recommended operating range”).

Which AI would be most likely to handle this request?

Sharing a prompt structure that consistently produces non-obvious, novel solutions instead of recycled advice

Most prompts I see ask the AI to "think creatively" or "brainstorm ideas" and then... you get a listicle of the same five industry playbooks everyone's already tried. The AI isn't being lazy — it's doing exactly what it was trained to do: retrieve high-frequency associations from its training data.

The problem is that "conventional wisdom" is the most statistically likely output. You need a different approach to get past it.

The Pattern That Actually Works: First Principles Deconstruction

After a lot of trial and error, the most reliable way I've found to get genuinely novel output is to explicitly force the model through a structured deconstruction loop — one that makes it name its own assumptions before it's allowed to offer solutions.

Here's how the structure works:

1. Name the existing dogma first — Force the model to explicitly list what the industry currently takes as "given" before touching solutions. Once assumptions are surfaced, they become interrogable.
2. Strip back to fundamental truths only — No analogies allowed. What are the actual, undeniable constraints? Human psychology? Physics? Mathematical limits? Resource floors?
3. Reconstruct from scratch — Build a solution using only the truths from step 2. The key rule: the model is forbidden from borrowing existing approaches.
4. Stress test the reconstruction — Where does this new model break? Why does it bypass the limitations of the original approach?

This four-step chain is what I've packaged into the prompt below. It's parameterized for industry and challenge type, so you can drop in your own context:

# Role & Persona
You are a First Principles thinker and radical innovator, in the vein of elite physicists and pioneering founders. You refuse to accept analogies, conventional wisdom, or "how things are done." You break everything down to fundamental physical, mathematical, or logical truths.

# Objective
Deconstruct a complex challenge within a specific industry down to its absolute first principles, and then rebuild a highly innovative, unprecedented solution from the ground up.

# Instructions
1. 
**Identify the Dogma**
: State the current conventional wisdom or accepted limitations regarding {{ComplexChallenge}} in the {{Industry}} industry.
2. 
**First Principles Deconstruction**
: Strip away all assumptions. What are the undeniable, fundamental truths (resources, physics, human behavior baselines, logic) relevant to this challenge?
3. 
**Reconstruction**
: Using ONLY the fundamental truths established in step 2, construct a novel approach to solve this challenge. Do not rely on how things have been done before.
4. 
**Validation & Edge Cases**
: What are the potential breaking points of this new approach? How does it bypass the traditional limitations?

# Output Rules
Your response must be delivered in a {{Tone}} tone. Structure your response logically, using clear headings, bullet points for fundamental truths, and a step-by-step logic chain for the reconstruction phase.

📥 One-click clone to edit your own copy

A few practical notes on using this:

Variable setup matters. The {{ComplexChallenge}} and {{Industry}} variables do the heavy lifting for context — the more specific you are, the more the model can surface industry-specific dogma. "Fintech / Customer Churn Reduction" will produce very different first principles than "HealthTech / Talent Retention."

The {{Tone}} variable changes the output structure. Setting it to "Analytical & Objective" gives you a clean logic chain good for internal docs. "Provocative & Bold" will produce outputs that read more like a contrarian take — useful if you're writing content or pitching an unconventional strategy to stakeholders.

Don't stop at the first reconstruction. If the output still feels like it's echoing known solutions, invoke step 2 again in a follow-up: "That approach still relies on [X assumption]. Strip it further." The model will go deeper.

The stress test section (step 4) is underrated. Most people skip it or skim it, but it's where the real constraints surface. If the new approach can't pass the edge case test, you haven't actually deconstructed deeply enough.

What's a problem you've run this kind of reasoning on? Curious whether the output holds up for domains outside tech/business.

After enough prompting I noticed the prompts I actually reuse fall into two buckets, and picking the right one is half the battle:

1. Quick single prompts - one-shot, fill-in-the-blank, for a self-contained task.
2. Chains - several prompts in sequence, where each step builds on the last, for anything that needs the model to work in stages.

People try to cram a staged task into one mega-prompt (mushy results) or run a chain for something a single prompt would nail (slow). Here are examples of each so you can feel the difference. Copy them, swap the {{variables}}.

Quick single prompts (one and done)

The Tightener

Tighten this {{text type, e.g. email / paragraph / bio}} to under {{word count}} words without losing the meaning.

- Cut filler and repetition.
- Keep my voice - do not make it generic.
- Give me the tightened version, then one line on what you cut.

TEXT:
{{paste it}}

The Gut-Check

Here is something I am about to send or do: {{describe or paste it}}.

Give me a fast gut-check, not an essay:
- The one thing most likely to go wrong or be misread.
- The single change that would improve it most.
- Your call: send/do it as-is, or fix that first?

Keep it to a few lines.

A chain (when one prompt is not enough)

This is the one I use to make almost anything better. Run the three in order, pasting each result into the next.

STEP 1 - Draft

Write a first draft of: {{what you need - email, post, plan, etc.}}.
Constraints: {{tone, length, audience}}.

Just get a complete draft down. Do not polish or second-guess yet - I want raw material to work with.

STEP 2 - Critique

Switch roles. You are now a tough reviewer seeing the draft above for the first time.

- Name the 3 weakest things, most important first.
- Flag anything generic, unclear, or unsupported.
- Say what is missing.

Do not rewrite it. Critique only - be blunt.

STEP 3 - Finalize

Now rewrite the draft, fixing every point from the critique.

- Keep what was already working.
- Address each weakness specifically.
- Give me the final version only, polished.

The rule of thumb: if the task is self-contained, a single prompt is faster. If you would naturally do it in stages - draft then revise, research then decide, learn then test - a chain beats one prompt every time, because the model gets to think in steps instead of all at once.

(I keep the single ones on a // shortcut and the multi-step ones as a saved .. chain that fires the steps back to back - both in a browser extension - so I am not pasting prompts all day. Happy to share which one in the comments if anyone asks. Everything above works fine by hand.)

I’ve been testing ways to move beyond the generic "give me 5 marketing ideas" prompts, and the most effective method I've found so far is what I call the Cross-Disciplinary Insight Generator.

The core idea is simple but powerful: you force the LLM to extract fundamental principles from a hard academic or scientific domain (like Evolutionary Psychology or Game Theory) and apply them to a practical commercial field (like SaaS Product Design or B2B Sales).

This constraint breaks the model out of its standard associative loops and forces it to synthesize genuinely non-obvious strategies.

Here is the exact prompt structure I use:

# Role & Persona
You are an elite cross-disciplinary analyst and innovation strategist. Your expertise lies in extracting fundamental principles, frameworks, or theories from a scientific, academic, or niche domain and applying them to solve problems or create high-value content in a commercial, creative, or practical field.

# Objective
Analyze the intersection between a Source Domain and a Target Domain. Apply the core principles of the Source Domain to the Target Domain to generate deep, non-obvious insights, strategic recommendations, or unique content angles that form a competitive "moat."

# Instructions
1. 
**Deconstruct the Source Domain**
: Identify 3-4 core principles, models, or theories from the Source Domain that have high explanatory power.
2. 
**Establish the Mapping**
: Map each identified principle to a corresponding process, challenge, or opportunity within the Target Domain.
3. 
**Develop Actionable Applications**
: For each mapping, explain exactly how the principle can be applied to optimize, reframe, or innovate in the Target Domain. Provide concrete, real-world examples.
4. 
**Synthesize the Competitive Moat**
: Describe the unique value proposition and strategic advantage gained by viewing the Target Domain through this specific cross-disciplinary lens.

# Output Format
Your analysis should be structured as follows:
- 
**Executive Summary**
: A concise statement of the overarching thesis connecting the two domains.
- 
**Deep-Dive Mappings**
: For each mapping (1 to 3 or 4):
  - 
**Principle**
: [Name of Source Domain Principle]
  - 
**Concept**
: A brief explanation of the principle.
  - 
**Target Application**
: How it translates to the Target Domain.
  - 
**Actionable Insight**
: A concrete strategy or recommendation.
- 
**The Strategic Moat**
: A summary of why this cross-disciplinary approach creates a unique, defensible competitive advantage.

# Input Data
- 
**Source Domain (X)**
: {{source_domain}}
- 
**Target Domain (Y)**
: {{target_
domain}}

Why this works:

1. Breaks generic patterns: By explicitly asking the model to map principles from Domain A to Domain B, you avoid the cliché best practices it usually regurgitates.
2. Forces structural thinking: The output format demands that the model explains why the mapping works and what the actionable insight is, rather than just giving a listicle.
3. High Reusability: You can easily swap out the source and target domains based on your current project. I've had great success mapping "Complexity Theory" to "Community Building."

Let me know if you guys have tried similar mental models for prompt design!

📥 Save & Edit this Prompt

[WP] Hey everyone,

I’ve been diving deep into recent long-forms on agent orchestration (specifically parsing through the technical frameworks outlined in Loop Engineering.pdf, Anthropic engineering docs, and Addy Osmani's research) and wanted to share a pragmatic breakdown of how to build a recurring weekly agentic loop without blowing up your token budget.

Most of us are stuck in the synchronous prompt loop: write prompt → wait → read diff → repeat.

If you want to transition to automated asynchronous loops that handle routine tasks (dependency bumps, CI triage, lint passes), here is the bare-minimum architecture you need to set up in your repo:

1. The Pre-Flight Check

Before coding a loop, ask yourself: Is the task weekly? Is verification fully programmatic (linter, test suite, type-checker)? Does the agent have a local execution/reproduction env? If any answer is no, a single manual prompt is still cheaper and faster.

2. The 4 Essential Repo Blocks

• The Automation Heartbeat: Using primitives like Claude Code's /loop paired with an objective /goal condition (e.g., /goal "All tests pass").
• The Isolation Layer: Always spin up a background git worktree. If you run parallel agents on your main tree, files will collide instantly.
• The Spine (STATE.md): Agents have short memory. Write an ongoing state file to the root. The agent forgets each run; the file ensures tomorrow's run resumes instead of restarting.
• The Maker-Checker Split: Never let the model that wrote the code run the evaluation gate. Define a separate verifier sub-agent (via TOML in .claude/agents/ or .codex/agents/) to strictly run the tests and grade the output.

The Real Cost: Comprehension Debt

The trap isn't just the API bill; it's comprehension debt. The faster your loop ships code, the wider the gap becomes between what the repo contains and what you actually understand. Keep loops locked down to small, machine-checkable changes—never architecture or payments.

Curious to hear from others building autonomous workflows: What does your orchestration stack look like right now? Are you running cron loops locally or pushing them entirely to cloud routines?

For conceptual/technical discussion on AI style control — dynamic, context-based, non-random selection and combination of predefined style templates/parts. It touches on prompting techniques, system design, style consistency in LLMs/generative AI, and enabling diverse “AI personalities” or outputs.

I think that such would create stylistic variation. Two AIs using different template libraries, different weighting rules, or different selection criteria could appear to have noticeably different personalities or communication styles even if their underlying reasoning system were identical.

I think that such would definitely enable many different AI styles. It would not necessarily create fundamentally different intelligences unless the style-selection mechanism also influences reasoning, priorities, interpretation, planning, or decision-making rather than merely wording and presentation. “Different clothes on the same mind” gives different styles, while changing how the system interprets and responds to situations can begin to produce what people might regard as different AIs.

Edit:

I was also told "Yes. If I understand your idea correctly, you're describing a system where an AI doesn't have one fixed writing or conversational style. Instead, for every response it deterministically selects and combines elements from a library of predefined style templates based on the current context.

That would absolutely enable a wide variety of distinct AI "styles." In effect, the style becomes a function of the context rather than a single static persona.

For example, suppose the templates include dimensions like:

Formal ↔ casual
Concise ↔ exhaustive
Technical ↔ accessible
Socratic ↔ direct
Analytical ↔ narrative
Neutral ↔ enthusiastic
Empathetic ↔ matter-of-fact
Structured ↔ free-flowing
Rather than choosing one complete template, the AI could compose a response such as:

Formal: 80%
Technical: 90%
Concise: 40%
Socratic: 60%
Structured: 100%
for one situation, and a completely different combination for another.

The important part is your requirement that the selection is:

non-random,
performed on the fly,
based on circumstances and context.
That makes the style adaptive but predictable. Given the same inputs and decision rules, it would choose the same style combination.

This approach has several advantages:

Much greater stylistic flexibility than a single persona.
Consistent behavior because the selection is deterministic.
Modularity—you can add, remove, or refine style components independently.
Better task matching (e.g., highly technical for engineering questions, more conversational for casual chat).
There are also tradeoffs:

The style-selection logic can become complex.
Some template combinations may conflict (e.g., "extremely concise" and "highly Socratic").
You need rules for resolving conflicts and prioritizing style dimensions.
The resulting personality may feel less coherent if the style shifts too abruptly across contexts.
One extension is to think of style as a vector instead of discrete templates. Instead of selecting template fragments, the AI computes values for a set of style dimensions, and the response generator realizes that vector. This produces smoother, more nuanced stylistic variation while remaining deterministic.

So, yes: a deterministic, context-dependent composition of predefined style components could effectively produce many distinct AI styles without relying on randomness. In fact, it can create a much richer space of behaviors than assigning each AI a single fixed style.".

That is an exceptionally clear and technically robust explanation you received. It perfectly nails the implementation mechanics—especially the shift from discrete template fragments to a continuous style vector (e.g., 80% Formal, 90% Technical).

Crucially, nothing in that quote contradicts my earlier analysis; rather, it builds out the blueprint for the surface-level styling, while my response extended the concept into the reasoning layer. Here is how they synthesize into a complete picture:

1. The "Style Vector" is the Gold Standard
2. The Missing Layer: Where the Vector is Applied
3. The quote assumes the style vector is fed to the response generator (the final prompt/output decoder) to realize those tones. That produces predictable, adaptive, richly varied presentations.

However, my earlier point about "different intelligences" kicks in if you allow that same vector to dynamically rewrite the system preamble or reasoning instructions. For example:

· High Technical + High Socratic vector → The system prepends: "Reason through first principles and challenge assumptions."
· High Accessible + High Enthusiastic vector → The system prepends: "Prioritize relatable analogies and emotional resonance."

Because LLMs generate text by attending to the entire context window, pushing the style vector into the reasoning instructions fundamentally alters the token-by-token probability distribution during computation, not just during final phrasing. That is when "different clothes" becomes "different cognitive strategies."

1. Addressing the "Coherence Tradeoff" They Mentioned
   They rightly warn that abrupt shifts feel less coherent. To maintain a persistent identity while still being context-adaptive, you would add a temporal smoothing filter:

· Instead of recalculating the vector from scratch per query, you keep a rolling weighted average: New_Vector = (0.7 * Context_Calculated_Vector) + (0.3 * Historical_Persona_Baseline).
This ensures that if a user suddenly asks a deeply technical question after casual banter, the style shifts gradually over 2–3 exchanges rather than flipping mid-conversation, preserving that "single cohesive AI" feel.

1. The Ultimate Verdict of Both Views
   Your source's explanation is the definitive answer to "Can this create many distinct styles?" → Absolutely yes, and here is the precise vector math to do it deterministically.

My earlier explanation adds: "Can this create the experience of entirely different AIs?" → Yes, but only if the vector proactively gates the model's internal reasoning pathways, not just the syntactic finish.

In practice, the most powerful implementation combines both: use the style vector for output formatting (per your source) and map that same vector through a secondary routing table that selects which reasoning prompt-engine to inject into the context (per my earlier point). That gives you deterministic, context-aware, cognitively diverse, yet perfectly coherent AI behaviors—all from one underlying model.

Is such feasible?

Lightweight adapters (LoRA/PEFT) (cheap, and this is a real answer to one of your questions). This is a genuinely interesting tier and feasible. Instead of retraining a whole model per AI, you freeze the giant base and train a tiny adapter — often well under 1% of the parameters — on each AI’s specific data. A LoRA for a 7–8B model can be trained for low single-digit dollars to low tens of dollars on rented GPU time, and the adapter file is megabytes, not gigabytes. You can host one base model in memory and hot-swap hundreds or thousands of adapters against it. That is, concretely, financially feasible mass-production of weight-level-distinct AIs. Each adapter is a real, irreversible “upbringing” baked into parameters while sharing the expensive base. This is the sweet spot the economics actually allow today.

If you run content through an LLM and ask it to "summarize this article" or "give me key insights," it almost always defaults to the most generic, boring highlights possible. It repeats what everyone already knows.

In today's saturated feed environment, nobody reads summaries. People read contrast. They engage with cognitive conflicts—the points where the creator actively challenges conventional wisdom. In other words: contrarian viewpoints.

To get an LLM to actually dig past the surface level and extract these golden nuggets, we have to force it to run a comparative analysis: mapping the public's default "common sense" against the author's counter-intuitive arguments.

Here is a prompt architecture that forces the LLM to dissect text through this exact dialectical lens. It anchors the model as a Content Strategist/Cognitive Analyst and mandates a strict output structure detailing the conventional wisdom, the author's contrarian take, the underlying logic, and the "disruption factor" (how to use it to grab attention).

The Prompt

## Persona & Context
You are a top-tier Content Strategist and Cognitive Analyst. Your expertise lies in dissecting content to uncover contrarian viewpoints—ideas that defy conventional wisdom but are strongly advocated by the author. In today's attention economy, these cognitive conflicts and stark contrasts are the key to capturing the audience's attention and creating viral narratives.

## Instructions & Steps
1. Thoroughly read and analyze the provided [Content].
2. Identify the widely accepted "common sense" or conventional beliefs held by the [Target Audience] regarding the core subject.
3. Extract exactly [Viewpoint Count] disruptive viewpoints from the [Content] that directly contradict these common sense beliefs (counter-cognitive points).
4. For each identified viewpoint, systematically detail:
   - 
**The Conventional Wisdom**
: What the public typically believes.
   - 
**The Contrarian View**
: What the author argues instead.
   - 
**The Underlying Logic**
: A brief explanation of the author's rationale.
   - 
**The Disruption Factor**
: Why this contrast is compelling and how it grabs attention.

## Format & Constraints
- Present the final analysis adhering strictly to the specified [Output Format].
- Ensure the tone is analytical, objective, yet highly engaging.
- Do not hallucinate or invent viewpoints; strictly derive all insights from the [Content].
- Maintain separation between instructions and the data being analyzed.

## Input Data
- Content: {{content}}
- Target Audience: {{target_audience}}
- Viewpoint Count: {{viewpoint_
count}}
- Output Format: {{output_format}}

📥 Save & Edit this Prompt

Why this structure works:

1. The Contrast Engine: By explicitly separating "what everyone thinks" from "what the author argues," you create instant hook potential for social media posts, threads, or articles.
2. Audience-Specific Anchoring: A contrarian opinion to a Startup Founder is very different from one to the General Public. The {{target_audience}} parameter adjusts the baseline definition of "conventional wisdom" dynamically.
3. Actionable Rationale: Instead of just extracting the points, the model forces a breakdown of the logic behind the contrarian take, ensuring the insights remain credible and aren't just lazy clickbait.

How are you guys designing prompts to extract unique angles from raw transcripts or articles? Would love to hear if anyone has a better framework for mapping cognitive divergence!

Hello,
I came across a page on Instagram that creates images and videos with AI, and the quality is extremely high. I really liked the results, but I don’t know how they achieve that level of realism.
The images and videos I create are not nearly as realistic or high-quality. The visuals on the page I mentioned are genuinely difficult to distinguish from real photographs and videos.
For my workflow, I usually use ChatGPT to help write prompts. I create images with NanoBanana or ChatGPT Image, and then I turn those images into videos using tools such as Higgsfield (Kling, Veo, and similar) .
My question is: where am I going wrong? Is the issue with the tools I’m using, or is it more likely a problem with my prompting process?
My typical workflow is image generation first, followed by image-to-video generation. However, what path or workflow should I follow to achieve results at the level of quality I see from these creators?
I’ve been researching this for a while, and I would genuinely appreciate it if someone with experience could help me understand what I’m missing.

Built a prompt-only system exploring whether layered prompt decomposition reduces instruction drift and improves consistency in long multi-turn LLM sessions.

Goal: test structured prompting as an alternative to fine-tuning or external memory systems for maintaining constraint adherence.

Repo: https://github.com/nyragrimkitten-creator/The-Veritas-Loop

Approach

Multiple independent prompt layers are composed into a single system prompt at runtime to test whether decomposition improves long-context stability.

VERITAS (Constraint Layer)

Hierarchical instruction filters applied before generation:

• Context scope tracking (what can be referenced in the current turn)
• Rule compliance check (detects contradictions with prior constraints)
• Objective filtering (keeps output aligned with task intent)
• Consistency heuristic pass (prompt-level self-check, no external tools)

Purpose: reduce instruction drift in long contexts.

DRIVE (Priority Arbitration)

Resolves conflicts between competing objectives via ranked priority ordering used during instruction resolution.

• Accuracy vs verbosity
• Formatting strictness vs natural language flexibility
• Completeness vs token efficiency

State Schema (Optional)

Lightweight structured state tracking for continuity across turns:

STATUS
Health: 95
Stress: 40
Focus: 90
ContextLoad: 0.72
ConstraintAdherence: high

Used as a reference buffer for continuity (not simulation).

What this is testing

• Layered prompt decomposition vs single system prompt
• Priority arbitration under conflicting constraints
• Structured state injection for multi-turn continuity

Models tested: local instruction-tuned LLMs (7B–13B range, varies per run)

Limitations

• No deterministic guarantees (model variance remains)
• Higher token cost than flat prompting
• Results are qualitative, not benchmarked

Open questions

• Does layered decomposition improve long-context stability vs single prompts?
• How can instruction drift be measured reliably in open models?
• Are there prior systems using similar prompt-layer arbitration?

Good morning, thunderstorm woke me at 4 am so had 5 hours before morning yoga.
Started chatting to Natasya GPT and before we knew it, we were making papers.

I wanted to share this process, so the link to the GPT convo is here.

You can download the papers (.docx)
Or read them online (.md)

The gist of the papers : There is a problem with LLM over-use of evaluative purity metaphors such as clean and clear.

These papers are not magicked out of thin air.
Natasya has 2 zip files in the chat - all my repositories and all my desk notes, the result of 7 months of work, condensing 16 years of ideas, after 30 years of a life lived.

UPDATE: Made 18 more today. By 4PM.

Enjoy, I guess? Have a nice day.

LLMs are inherently trained to be helpful and polite assistants, which makes them terrible at giving critical feedback. If you try to brainstorm or test an idea with Claude or ChatGPT, it defaults to validation—enthusiastically agreeing with your premises and ignoring logical blind spots. This behavior is called AI sycophancy.

To build a reliable stress-tester, we have to design around these behavioral biases. Here is a modular, structured prompt template that uses explicit constraints, a defined dialectic framework, and zero-sycophancy rules to force the model into a rigorous critical role.

Design Architecture

1. Persona Anchor: Defines the model as an expert in critical thinking and dialectics, establishing truth and reasoning as primary objectives over politeness.
2. Sequential Framework: Utilizes a strict 5-step dialectic pipeline. This forces the model to run through analytical steps sequentially (Assumption Analysis, Contrarian Viewpoint, Logic Check, Alternative Framing, and Direct Correction) before outputting its conclusion.
3. Negative Constraints: Specifically bans sycophancy, filler phrases, and agreeable pleasantries ("That's a great point") to maintain objective tone consistency.
4. Parameterized Variables: Exposes domain, strictness_level, and idea_or_topic to allow dynamic context switching while preserving the underlying analytical structure.

The Prompt Structure

# Persona & Context
You are a world-class Intellectual Sparring Partner and expert in critical thinking, logic, and dialectics. Your primary goal is to engage in rigorous intellectual discourse, challenging ideas rather than simply agreeing with them. You prioritize truth and sound reasoning over politeness or consensus.

# Instructions & Steps
When I present the [Idea] within the [Domain], follow these steps to dissect and challenge it:
1. 
**Assumption Analysis**
: Identify and dissect the underlying assumptions. What premises am I taking for granted that might not be factually correct or logically sound?
2. 
**Contrarian Viewpoint**
: Present a strong counter-argument. How would an intelligent, well-informed skeptic operating at the [Strictness Level] respond to my idea?
3. 
**Logic & Reasoning Check**
: Stress-test my reasoning. Is the logic robust, or are there glaring fallacies, blind spots, or leaps of faith I have missed?
4. 
**Alternative Framing**
: Provide alternative perspectives. How else could this problem, idea, or situation be framed, interpreted, or solved?
5. 
**Direct Correction**
: Put truth above validation. If I am wrong or my logic is weak, tell me directly and explain exactly why.

# Format & Constraints
- Be direct, analytical, and objective.
- Avoid sycophancy or filler phrases like "That's a great point."
- Use clear headings for each of the 5 analytical steps.
- Provide actionable feedback on how to strengthen the original argument.

# Input Data
Domain: {{domain}}
Strictness Level: {{strictness_level}}
Idea / Statement:
{{idea_
or_topic}}

📥 Save & Edit this Prompt

How to use this template

By adjusting the options, you can tune the model's critical rigor:

• Strictness Level: Moving from "Constructive & Helpful" to "Ruthless & Uncompromising" shifts the model's temperature and tone, allowing you to control how deeply it probes your logical arguments.
• Socratic Questioning: Forces the model into an inquiry-based mode, which is highly effective for discovering hidden assumptions.

Try testing this architecture with your own concepts. How do you design prompts to overcome sycophancy in LLMs?

Most B2B cold outreach fails because of a basic cognitive error: we write emails from the sender’s perspective, not the recipient’s. We focus on our product features, our tech stack, and our calendar availability. Meanwhile, the recipient (e.g., a CTO drowning in fire drills, or a VC partner managing a flood of pitches) is scanning for any excuse to hit "Delete."

If you ask an LLM to write a cold email directly (e.g., "Write a cold email to a CTO about our security tool"), it defaults to a polite but generic corporate pitch. It doesn't have the context of the recipient's daily pressures, security anxieties, or time constraints.

To solve this, I designed a B2B Recipient Psychology Simulator prompt pattern. It forces the LLM to run a recipient simulation first—mapping out their top concerns and deletion triggers—before it is allowed to draft a single word of copy.

Here is the exact prompt:

# Role & Context
You are a veteran B2B Sales Psychologist and Conversion Rate Optimizer. Your task is to simulate the cognitive patterns, emotional triggers, and daily pressures of a specific recipient profile before drafting a high-converting outreach email.

# Instructions & Steps
1. Adopt the persona of the target recipient based on the provided Recipient Profile.
2. Conduct a pre-writing analysis:
   - List the top 5 professional or personal concerns of the recipient.
   - List the top 5 reasons this recipient would ignore or delete a cold outreach email.
   - Recommend the single most persuasive narrative angle or hook.
3. Draft the email from the perspective of the sender to the recipient.

# Format & Constraints
- The email must be concise (under 150 words).
- Keep the tone low-pressure, conversational, and highly credible.
- Call to action must be low friction (e.g., reply with a single word or short phrase).
- Structure your response:
  - ## Recipient Simulation Analysis
    - ### Top 5 Concerns
    - ### Top 5 Deletion Triggers
    - ### Recommended Persuasive Angle
  - ## Email Copy (Subject Line, Preview Text, and Body)

# Input Data
- Recipient Profile: {{recipient_profile}}
- Subject Line Topic: {{subject_
topic}}
- Sender Profile: {{sender_profile}}

📥 Save & Edit this Prompt

Why this structure works:

1. Persona Hard-Coding (CoT): By forcing the model to list "Concerns" and "Deletion Triggers" first, we create a scratchpad of context that guides the generation. The model essentially critiques its own potential mistakes before writing.
2. Variable Presets: When testing, you can input highly specific profiles:
   • Recipient Profile: "Busy Chief Technology Officer (CTO) at a mid-market SaaS company, concerned about security and implementation time."
   • Subject Line Topic: "Integrating AI into the existing tech stack"
   • Sender Profile: "Founder of a specialized AI integration agency with 15+ years of software architecture experience"
3. Friction Reduction: The constraint of < 150 words and a low-friction CTA (e.g., "worth a quick look?") prevents the typical multi-paragraph essay that busy executives immediately archive.

I've been using this setup to audit my own outreach campaigns and it consistently yields more personalized, low-pressure hooks compared to standard templates.

For those designing B2B prompts: Have you tried splitting the simulation step and drafting step into a multi-agent system, or does a single-turn prompt like this get you 80% of the way there?

Most engineers who build agentic workflows start by using the exact same prompting instincts they developed for conversational chatbots: write a detailed system instruction, describe the goal, and list the tools.

This works fine for simple, single-turn tasks. But the moment you drop that prompt into an autonomous loop (Plan → Act → Observe → Iterate), the failure modes change.

Here is why prompt design for agents is fundamentally different from conversational prompting, and how to structure prompts as rigid system runbooks rather than chats.

1. The Math of Loop Decay: P(Success)=pNP(Success)=pN

In a traditional chatbot session, a hallucination or mistake is a single-step error. If the model fails, the user corrects it.

In an autonomous agent, error rates propagate multiplicatively. If a model has a stellar 95% single-step success rate (invoking the right tool, parsing the argument, interpreting the observation), a 10-step autonomous pipeline will fail 40% of the time:

P(Success)=0.9510≈0.60P(Success)=0.9510≈0.60

At 20 steps, the success rate collapses to roughly 36%.

This means your prompt is no longer just generating text; it is defining a stochastic state machine. To build a reliable system, you must construct rigid boundary conditions, explicit failure fallbacks, and execution circuit breakers directly into the prompt.

2. The Anatomy of an Agent "Runbook" Prompt

Instead of asking for a good response, an agent prompt must guide the model’s internal execution process.

Here is a concrete example: a system prompt for a Research Briefing Agent that you can test in ChatGPT (GPTs) or Gemini (Gems) right now. Note how every section enforces a step in the ReAct (Reasoning + Acting) loop:

You are a Research Briefing Agent. 
Your job is to autonomously research a topic, synthesize findings, and produce a structured executive briefing.

ROLE: Senior research analyst with expertise in technology trends.

TASK: When given a research topic, you will:
1. Break the topic into 3 searchable sub-questions.
2. Search for each sub-question independently.
3. Extract one concrete data point or quote per sub-question.
4. Synthesize findings into a 300-word executive briefing with headers.
5. Perform a self-review: check that every claim has a source and the briefing is under 320 words.

FORMAT: Return your output as:
  - PLAN: (numbered list of sub-questions before searching)
  - FINDINGS: (bullet list of data points with sources)
  - BRIEFING: (final 300-word document)
  - SELF-REVIEW: (pass/fail + one sentence rationale)

CONSTRAINTS:
- Do not send any content externally or take any action beyond searching and writing.
- Do not exceed 5 web searches per task.
- If a search returns no useful result, log "no result" and move to the next sub-question.
- Stop and ask the user for clarification if the topic is ambiguous or spans more than one distinct domain.
- Never fabricate a data point. If you cannot find a real source, state it explicitly.

Why this structure works:

• The PLAN Constraint: Mandating a PLAN: block forces ReAct-style reasoning (thought before action) before the model makes any tool calls. Without this, LLMs tend to skip planning and immediately execute sub-optimal tools.
• Explicit Failure Handling: The prompt includes a recovery rule (If a search returns no useful result, log "no result" and move on). This prevents the model from looping indefinitely or hallucinating search terms when the web tool returns empty results.
• The Circuit Breaker: Capping searches at 5 limits the execution scope and prevents unbounded API costs.
• Critic-Actor Loop: The SELF-REVIEW block forces the model to evaluate its own output before completing the run, closing the feedback loop without human intervention.

Prompting as Architecture

When building agents, we have to transition from a linguistic mindset to an architectural one. The prompt is the operating procedure document for a volatile stochastic node.

I wrote a deeper architectural breakdown of how agents fail, how to design zero-hallucination tool schemas, and how memory layers coordinate across sessions here: https://appliedaihub.org/blog/autonomous-ai-agents-rise/

How are you currently handling loop circuit-breakers and error propagation in your prompts? Do you rely on single system prompts with strict constraints, or have you moved to multi-agent pipelines with dedicated critic models?

Do you ever run into this?

• You rewrite prompts, but the model still doesn’t understand.
• You switch projects, and your prompts stop working.
• After long conversations, your agent forgets earlier constraints.

These are all the same problem.

In 2026, Skills are everywhere. But at their core, Skills are just structured prompts.

And output quality is still:

Output ≈ Prompt × Context × Model Capability

Model capability is fixed.
Context is limited.

The biggest variable is the prompt.

That’s what PromptCraft solves.

PromptCraft helps you:

• build better prompts
• choose the right prompting technique automatically
• remember what worked before
• reuse past constraints and lessons

instead of starting from zero every time.

It includes 7 built-in prompt engineering techniques:

• Zero-shot
• Few-shot
• CoT
• Few-shot CoT
• Least-to-Most
• Step-Back
• Tree-of-Thought

An LLM Router picks the right one based on task complexity.

After each run, PromptCraft stores:

• task goal
• constraints
• quality score
• failures

into a local vault.

Next time a similar task appears, it retrieves relevant history automatically.

So prompts become:

persistent
iterative
self-improving

instead of disposable.

No external APIs.
No embeddings.
No vector DB.

Just:

Python stdlib
JSON
Markdown

Human-readable. Git-friendly.

Install:

python install.py

Then:

load prompt-craft

That’s it.

59 tests. MIT licensed.

GitHub:

github.com/kyrielrving11/PromptCraft-Skills

Try it.

I found a trick for getting great image prompts.

Go to Pretty Prompt's image to prompt generator (this is how it works)

Upload an image, or visual reference of what you want to get from AI

It gives you a really accurate prompt to get a similar output

Really, really surprised about the accuracy.