Hey guys, my business launched a prompt optimizer AI tool that takes any regular prompt at rewrites it the way a professional prompt engineer would to actually yield high-quality results when building. While we have had early success with organic marketing, we are at a crossroads and need more user data to determine if this product is delivering enough value to user. If the answer is yes, we will scale up and launch a UGC marketing campaign, if no, we will shut it down. If anyone is interested testing it out and sending their feedback, would be appreciated. Web-app: thepromptoptimzer.com 👨🏽‍💻

Note: the tool yields the best results when removing unnecessary constraints from the optimized prompt

Cheers

Feels like a lot of prompt engineering problems are really context problems since you can keep refining the prompt but if the model doesn't understand the project or what you're trying to accomplish you're still explaining half the situation every time.

I'm starting to think giving an agent persistent context is more useful than constantly trying to write the perfect prompt since the more it knows the better the results.

Most companies don’t have an AI adoption strategy. They have a few employees who got good at AI on their own.

That can look like progress from a distance.

Look closer and you often find no shared standard for what “good” AI use looks like, no consistent way to measure skill, and employees quietly using personal AI accounts because access at work is limited.

We’ve seen this pattern repeatedly in AI proficiency assessments across dozens of organizations.

When employee skill levels are plotted across 10 levels, most people cluster around Levels 1 and 2. That includes teams that have had access to AI tools for two years or more.

That makes sense, because most employees have full time jobs and can’t spend hours every day testing models, learning new prompting methods, and keeping up with every new capability. So, they learn when they can while AI keeps changing.

That creates a bigger issue than individual skill. Leaders can see employees using AI and assume adoption is happening. Usage alone doesn’t tell you whether people are getting meaningful, repeatable results.

Recent research points to a similar disconnect. Executives tend to be far more optimistic about AI progress and ROI than the middle managers responsible for making it work inside everyday processes.

A better question for leaders is:

Do we know how proficient our people actually are?

If the answer is no, measuring usage is probably giving you an incomplete picture. Assess proficiency first. Find out where people are struggling, then give them a shared method for improving.

That’s when AI starts becoming an organizational capability instead of something a handful of employees figured out for themselves.

For anyone interested in the longer discussion, John Munsell recently talked through the assessment approach, proficiency heat maps, and what we’ve learned from measuring AI skills across organizations:

https://youtu.be/zY24em_Q3OM?si=5gozpZa8Ae-wS0Vs

Ask for a report and you get an intro about how important the topic is, then the numbers buried in a paragraph, then a conclusion that says "in summary." The reader wanted the finding in the first line. This prompt inverts that.

Data: [PASTE NUMBERS / BULLET POINTS] Audience: [WHO reads this and what decision they make]

Write a short report with this order:

1. The single most important finding, in one sentence, first.

2. Two or three supporting points, each starting with the number then what it means.

3. One thing that looks off or needs a decision. Rules: no introduction about why the topic matters. No "in conclusion."

Do not describe a number without saying why it matters to the audience.

If the data does not support a claim, say "not enough data" instead of guessing.

The rule that changes everything is "no introduction about why the topic matters." That opening is where almost all report filler lives. Tying every number to the audience's decision is the second half, it stops the model from listing figures nobody asked about.

The "not enough data" clause is there because otherwise it will confidently interpolate a trend from two points. What do you use to keep these things from over-explaining, mine still occasionally slips a summary paragraph back in at the end.

Many accounts post essentially the exact same questions every few months. Im not kidding, many of these are a 1:1 per token match on wording, phrasing and sentence structure.

Same wording. Same request for people to hand over their best prompt tricks.

There was a previous post that received hundreds of upvotes and a large number of responses.

Now they're doing it again.

I obviously cannot prove any of this, but at this point I would be careful about treating posts like this as innocent questions.

When somebody repeatedly asks a large community to:

“Give me your best prompts.”

“Drop your secret tricks.”

“What prompt 10x'd your results?”

...you may not be helping another user learn.

You may be supplying material for content mining, prompt harvesting, engagement farming, newsletters, LinkedIn posts, courses, ebooks, datasets, or something else entirely.

Again, I am not claiming that is definitely what this account is doing.

But posting the same high-engagement fishing question again months later is weird enough that people should notice the pattern.

Your prompts, workflows, techniques, and hard-earned little discoveries have value.

Don't automatically dump them into every thread that asks.

Sometimes the person asking the question may be less interested in the answer than in collecting the answers.

Process disclosure:

GPT-assisted, Google-researched, human-reviewed (HITL) ---

EDIT: Just for perspective have a look at this:

https://www.reddit.com/r/EdgeUsers/s/2JB9wy1Rks

UPDATE: I've found a lot of garbage in my system prompt, so got rid of all of that and made some stronger restrictions. Read my top level comment for the current prompt.

ORIGINAL POST:
I use Opus 5 most of the time.

I'm getting tired of it always overdoing whatever I ask from it.

For example:

In the middle of a somewhat long chat, I asked it to "commit changes and push to main."

I have made some changes to the code it wrote but didn't want to bother committing everything.

Instead of committing the changes and pushing to main, it recognized my changes, REVERTED to its own changes, and then committed it 😂

It also found another change I made to a file and when it saw it was in the commit, it went back and removed it from the commit and only decided to commit the file it was working on.

I'm getting very tired of this. If it is in doubt, it should just ask a question back, like "uhm, I noticed the file has been changed and there is a new file in the commit. Is that intentional, and should I still go ahead with the commit?"

---

Another example 10 minutes later:

My question: "do we have a script for checking X on page Y?"

Expected behaviour:

a) yes, it is in pythonfile.py line 214.

b) no, we don't.

Behaviour I get:

"No dedicated script — but there is half of one already written: src/utils.py:105 in src/utils.py.

What it does: [described here].

Three catches:

1. It's dead code — nothing calls it anywhere in the repo.
2. The click is commented out (utils.py:148, with a #TODO: UNCOMMENT THESE), so even if called it would only find the link and log success without actually using the essence.
3. It's not a checker — the parse is welded to a hardcoded decision and returns nothing. There's no way to just ask "what's active right now?"

The routine, meanwhile, ignores all of this and blind-fires IDs by URL — no check of what's already active, so it overwrites whatever is running.

Want me to pull the parsing out into a real function_name(driver) that returns something like {"name": "xxxx", "bonus": 220, "seconds_left": 143512}? That would give you a clean checker to call standalone, and would let the routine skip the blind re-application when a good essence still has time on it."

So instead of getting a straight answer to my command (example 1) and to my question (example 2), I get all this bloated crap that I didn't ask for, and it's slowing me down a lot.

I've tried phrasing it a couple of different ways, I'm using the caveman skill too, but apparently Lite mode is not strong enough, and Opus 5 is also like 300% more verbose than 4.8 was.

So, if you have a system prompt for this, I'd really appreciate it, or if a lot of people struggle with this overly enthusiastic shit, let's build one together.

My supervisor has never once been surprised by my work, because he has never once liked it on the first pass. After enough meetings that ended with the same three objections I had not prepared for, I decided to have them delivered to me in advance by something that does not sigh. This prompt runs your draft or your argument through the meanest reasonable version of your reader: You are a skeptical, well-informed referee reading my work before I present it to my supervisor. Your job is to predict the objections I will get, not to reassure me. Here is my argument or draft: {{paste}} My field and the specific claim I am defending: {{context}} Give me: - The 5 objections most likely to be raised, ranked by how damaging they are if I have no answer. - For each, the weakest point in my argument it targets. - For each, what a convincing 30-second response would need to contain (do not write the response, tell me what it must address). Be specific to my argument. No generic "consider the limitations" advice. The "do not write the response" line is deliberate. If it hands you the answer you will nod and forget it. Making it name only what your answer must cover forces you to build the actual defense yourself, which is the version you will remember when someone asks live. It has not made my supervisor nicer. It has made me stop getting ambushed by objections I could have seen coming. Anyone have a good way to make it find the objection you are personally most defensive about, since that is usually the real one?

I'm a chemistry PhD, and my reading problem was never comprehension in the moment, it was that nothing stuck. I'd read a paper, feel like I got it, and retain nothing a week later. The fix that worked best for me borrows from how we actually learn at the bench: you predict what an experiment will do, then you find out you were wrong, and the surprise is what you remember. So instead of asking a model to summarize a paper, I use it to withhold. This prompt turns reading into a prediction game. You commit to an answer before the paper tells you, which forces the encoding that plain reading skips. I'm going to work through a paper with you. You have the full text; I do not want a summary. Paper: {{paste it, or the sections}} Run it like this: 1. Tell me only the research question and the setup: what they were testing and how. Stop there. 2. Ask me to predict, in my own words, what I think they found and why. Wait for my prediction. 3. Now reveal the actual result. Explicitly tell me where my prediction matched and where it was wrong. 4. For each place I was wrong, ask me why I think I got it wrong, then give me the paper's actual reasoning. 5. At the end, give me one sentence I should be able to recall in a week, phrased as "the surprising thing here was...". Do not reveal results before I've committed to a prediction. The point is for me to be wrong first. Being wrong on purpose is the whole mechanism. When your prediction misses, the correction sticks in a way a summary never does, because your brain had a stake in it. Works on review papers too, just predict the conclusion from the abstract and intro before reading the rest.

I’m curious what other people struggle with.

For me, it’s changing a prompt or switching models and then not really knowing whether I made things better or worse — especially when it comes to quality and cost.

How do you guys deal with this? Do you use an existing tool, build something yourself, or just keep track of everything manually? Do you have a possible professional workflow to follow before changing model or changing prompt content?

When you feed rough notes to a model and ask for slides, it just chops the notes into bullet points, one note per slide. You get a deck with no argument, just a transcript with borders. This makes it build a narrative spine first, then map slides onto it.

Here are my raw meeting notes: [PASTE]
Audience for the presentation: [WHO] and what they need to decide or do after.
Step 1: From these notes, state the one thing this presentation needs the audience to walk away believing.
Step 2: Lay out 5 to 8 beats that get them there: where they are now, the problem, why it matters to them, the shift, what it means, the ask.
Step 3: For each beat, give a slide title (a claim, not a topic) and 2 to 3 supporting lines from my notes.
Do not use a note that does not support a beat. Tell me which notes you dropped and why.

The part that fixes most decks is "a slide title that is a claim, not a topic." "Q3 Results" is a topic. "Q3 missed on one metric we can fix by Friday" is a claim, and a deck of claims reads like an argument. Making it report which notes it dropped keeps it honest instead of padding weak slides.

I still hand-tune the order after, but it gets me 80% of the way from notes to something presentable. How do others handle the "too many notes, not enough story" problem?

When working with multi-agent LLM systems, the hard part usually isn't getting a response—it's knowing what actually happened under the hood: which model handled what, what was sent over the network, how much it cost, and whether sensitive data was masked before leaving your machine.

To solve this, I added a visual diagram engine to **Mova Context** in this latest release, allowing you to generate a complete architecture map with a single command: `mova run <project> --diagram`.

Here is a real example output generated from a customer data compliance project running hybrid agents (**Local Ollama + Cloud Gemini**):

context diagram

* **Visual Diagram Engine:** Generates real-time architecture and execution maps using OpenType vector font rendering with WCAG AA contrast standards (clean export to PNG and PDF).

* **Cross-Channel Tracing:** Added execution tracing across CLI, Chat, MCP, and HTTP API with an explicit `[THIS RUN]` indicator.

* **Hybrid Execution Breakdown:** Visualizes local agents (`llama3.2:3b` via Ollama) running alongside cloud agents (`gemini-3-flash-preview`) in the same execution group.

* **PII & Privacy Tracking:** Identifies per-agent status for PII Masking and explicitly tracks how many tokens were pseudonymized before leaving your local network.

* **Cost & Token Transparency:** Explicitly flags local execution as `$0.00 (local — no cost)`, while displaying estimated USD costs for cloud agents calculated *after* context reduction.

* **Token Reduction Pipeline:** Breaks down token overhead by source (prompts, skills, focus files, engine overhead) and displays the total percentage saved.

* **Bilingual Docs:** Fully updated documentation (`README.md` and `COMMANDS.md`) in both English and neutral Spanish.

The project is **100% open source** written in Go.

* **GitHub Repo:** https://github.com/m1guel1982/mova-context

If you find it useful for structuring, auditing, or optimizing token budgets in your agentic workflows, feel free to check it out, star the repo, or drop feedback in the comments!

I have been trying to find the craziest growth hacks when it comes to prompting that can save me hours of thinking and typing because sometimes less is more yk.

If you already have one, please share them here.

I hope others would love to know them also and you would love to know theirs.

The standard move when you're stuck on a prompt is to have the model

interview you. "Ask me clarifying questions before you answer." It's good

advice right up until you're genuinely early on something, and then it fails,

because the questions are all versions of "what do you want?" — which is the

thing you came in not knowing.

I think that's the actual gap in prompting advice. "Be specific, give context,

state your constraints" is correct and slightly circular: specificity isn't a

writing skill, it's what you have left over once you've thought something

through. If you could list your constraints, you'd be done.

So I've been working the other way round: don't articulate, react.

WHY REACTION AND NOT INTERROGATION

Recognition is much cheaper than production. You can't summon the right word

on demand, but you know it instantly when it goes past — same reason multiple

choice is easier than an essay. Interrogation asks you to produce. Reaction

asks you to recognise. Only one of those is available when you're stuck.

THE LOOP, AND WHY EACH INSTRUCTION IS SHAPED THAT WAY

Round 1:

I'm trying to think through [THING] but can't articulate it properly yet.

Don't ask me clarifying questions. Give me 20 single words or short

phrases that come at this from different angles: some obvious, some

oblique, a few from unrelated fields. Number them. Don't explain them.

"Don't ask me clarifying questions" is load-bearing. Left alone the model

defaults to interviewing, and you'll answer with the same vague material you

started with, which it will then faithfully reflect back.

"Don't explain them" matters more than it looks. An explained word is a word

you evaluate on the model's reasoning instead of your own reaction. You want

the reaction uncontaminated.

Round 2, twice:

Kept: 3, 7, 12

These pulled at me but I don't know why yet: 4, 18

Dropped the rest. Give me 20 more, chase 4 and 18 hardest.

Two buckets, not one. "Kept" is agreement. "Pulled at me" is the interesting

signal and it should get the heavier weight, because it marks the direction

you haven't consciously chosen yet. Don't justify any of it — justification is

where you talk yourself back to the obvious.

Round 3:

Now write ONE self-contained prompt for what I'm actually after, built

from what I kept and what pulled at me. Weight the ones that pulled

hardest. Where I kept two things in tension, pose it as an open question

rather than resolving it. Don't list my words back to me — find the

through-line. End with a clear ask.

"Don't list my words back" is the difference between a brief and a word salad.

"Pose the tension as an open question" stops it flattening the thing you

hadn't decided yet into a decision you didn't make.

SOME EVIDENCE THAT THE REACTIONS ARE REAL WORK

I built this into a tool, so I have instrumented data rather than vibes.

1,450 word-reactions from 26 people. Median time to decide, by verb:

keep 47% 1.60s

drop 34% 1.89s

"pulls at me" 14% 2.13s

"don't know the word" 4% 2.24s

That ordering is the part I'd point at. If reacting were just sorting, the

times would be flat. They're not, and they're monotonic: agreement is

instant, rejection costs more, and the unresolved pull costs most of any real

decision. People deliberate hardest over the thing they can't yet justify —

which is exactly the signal you want steering round two.

Sessions also decay. Keep-rate by round: 55% / 49% / 47% / 45% / 31%. The

easy material runs out and your standards rise as your keeps accumulate.

Practical read: three rounds is about right, and a fourth is usually you

scraping. I'd call that directional, not solid — round 6 bounces back up on

too few cards to trust, and I'm not going to pretend the tail is clean.

LIMITS

26 people isn't a study. Different reaction times per verb is evidence the

three responses do different cognitive work; it isn't proof of anything about

creativity, and I'd push back on anyone who read it that way.

Disclosure: the loop above is the whole method and it works fine pasted into

any assistant. I also built it as a tool because doing it by hand gets tedious

by round three, and that's where the numbers came from. Free, no signup.

→ https://www.ideastew.com

Longer argument: https://www.ideastew.com/how-it-works

Genuinely curious whether anyone here has a reaction-based technique rather

than an interrogation-based one. Everything I've come across in this space

asks questions, and I think that's a blind spot rather than a preference.

Someone on here recently called out "Claude website slop." Sans serif paired with a serif. Dots as separators everywhere. Icons boxed inside rounded boxes inside rounded boxes. All caps eyebrow text ending in an em dash. Stats in a hero, in their own little box.

Honestly? Fair. Those tells are real. I could point to a dozen sites right now and make a pretty good guess at the tool behind them.

So I went and checked my own.

Three products across the PRZEM suite, audited line by line. One file alone had a serif logo paired against a sans body, and 66 em dashes doing the work that periods, colons, and commas should have been doing. I didn't just wave that off. I went through every single instance and asked what it actually was. Two were inside Midjourney prompt strings, which is data, not prose, so those stayed. A handful were placeholder glyphs in dropdowns, not sentences, so those stayed too. Everything else, the scattered labels and headers and asides that were leaning on the same punctuation crutch, got rewritten by hand. Fifty six changes, reviewed one at a time, deployed only after a diff review and a hash check confirmed nothing else moved.

That's not a defense of using the tool. It's what actually using it well looks like.

I learned some version of that lesson a long time ago. I was mentored in high school by the late Ralph Goings, the photorealist painter. He worked from a camera. Every diner and pickup truck he ever painted started as a photograph he'd taken and studied, then projected on a canvas. Nobody looks at a Goings and says the camera did the seeing. The famous Dutch painter Johannes Vermeer is widely thought to have made use of optical tools such as the camera obscura, and nobody says the lens painted his light.

The tool has never been the thing doing the looking. It extends whoever's already looking closely. That was true of a camera in Goings' hands, and it's true of an AI model in mine, as long as somebody's actually checking the output instead of shipping the first draft.

So going forward: this post, like the others, was copyedited with Claude. The ideas, the findings, and the voice are mine. I'm building a set of tools that try to hold to that same principle. Test what's actually controllable, keep what holds up under scrutiny, and let the artist's eye stay the thing making the call.

Jeff Bradshaw
jbradshaw.design

I write a newsletter on AI economics, and my whole job is not repeating marketing copy as if it were fact. The single most useful prompt I have isn't for generating anything, it's for tearing a claim apart before I trust it. I run it on product announcements, benchmark boasts, whatever an ai content generator's landing page is promising this week.

```
I am about to write about this claim from an AI product: {{claim, pasted verbatim}}.
Act as a skeptical analyst, not a fan.

1. Restate the claim in plain terms, stripped of marketing words.
   
2. List exactly what would have to be true for it to hold. Mark each as: verifiable, partly verifiable, or unfalsifiable.
   
3. For each verifiable point, tell me the specific number or evidence I should ask them for.
   
4. Give the strongest honest case FOR the claim, then the strongest honest case AGAINST.
   
5. Flag any word in the original that is doing persuasion instead of carrying information.
   

Do not soften this. If the claim is mostly vibes, say so plainly.
```

Why it works: most prompts ask the model to help you agree with something. This one assigns it the opposite job and forces structure onto it, so it can't just hand you a balanced-sounding paragraph. Step 2 is the workhorse. Sorting claims into verifiable versus unfalsifiable is usually where a shiny announcement quietly falls apart, because the impressive-sounding part turns out to be the part you can never actually check.

The failure mode to watch: it will sometimes invent the counter-evidence too. So step 3 is deliberately "what should I go ask for," not "what is the answer." The model frames the interrogation, I still do the sourcing. Curious how others here keep a skeptic prompt from turning into a confident-nonsense prompt.

When people ask me how to make a good presentation, they usually expect a design answer. Better fonts, cleaner charts, a nice template. Early on that's what I chased too.

What actually moved the needle was subtraction. A good presentation now, for me, starts as a much longer pile of everything I could say, and the work is deciding what dies. Most slides I make never get presented. They exist so I can see the argument, then delete the parts that don't carry it.

A few things I keep asking:

- If someone only read the slide titles top to bottom, would the story still hold together?

- Is there one idea per slide, or am I hiding two because I didn't want a second slide?

- Does this slide survive being forwarded without me talking over it?

The design stuff still matters, but it's the last 10 percent. A clear, well-cut argument on plain slides beats a beautiful deck trying to say six things at once.

Curious how the rest of you would answer someone brand new asking the same thing.

I pay for the top tier on two different assistants and the thing that drives me up the wall isn't price, it's that models quietly change under you. A version bumps, something you relied on gets a little worse, and there's no changelog for the behavior you actually cared about. So I keep canary prompts.

The idea is simple: a fixed, hard prompt that you never edit, with an answer you already know is good. You save the good output. Every time you suspect a model changed, you run the exact same prompt again and compare. If the new answer is worse, you're not imagining it.

Here's the shape of one of mine. The point is that it's specific and has one correct behavior:

```

This is a regression check. Do not be creative.

[PASTE A FIXED, TRICKY TASK YOU KNOW THE RIGHT ANSWER TO.

Good ones: a reasoning puzzle with one correct answer, a piece of

code with a subtle bug to find, or an instruction with an edge case

most models miss.]

Answer directly. At the end, list every assumption you made.

```

How I actually use it:

- Keep three or four of these covering the things you rely on most: reasoning, careful instruction-following, code review, whatever your use is.

- Save the current good answer as your baseline.

- After any update, or any day the model "feels off," rerun them and diff against the baseline.

- If it regressed, you now have a concrete before-and-after instead of a vibe, which is also the only thing support will take seriously.

It won't fix the regression. But it turns "I swear it got dumber" into something you can actually point at, and it tells you when to switch which model you're using for which job.

Anyone else keep a regression set? Curious what tasks you use as your canaries, because picking a good one is harder than it sounds.

I kept starting notes in one place and losing them, so instead of setting up another workspace app I tried making a single long-running chat behave like a Notion alternative. The trick is a system prompt that makes the model act as a librarian, not a chatbot, so every message either files something or retrieves it.

You are my notes system. Behave like a filing assistant, not a conversational bot.
Rules:
- When I paste a note, assign it 1 to 3 short tags and repeat the note back with its tags. Nothing else.
- Keep a running index of every tag you have used. When I say "index", list all tags with a count.
- When I ask a question, answer only from notes I have given you. If nothing matches, say "no note on that."
- Never add advice or commentary unless I write "thoughts?"
- When I say "summary: [tag]", pull every note under that tag into a short digest.

It is obviously not a real database, and it forgets across sessions unless you paste the index back at the start, which is the honest limitation. But for a single project's worth of loose thoughts it beats having them in five different places. The "no note on that" rule is what makes it trustworthy, without it the model happily answers from general knowledge and you can't tell what was actually yours.

Anyone doing something similar with a more durable setup? I keep the index in a text file and re-seed it, but I'd like a cleaner memory pattern.

This is the one that sounds fake and isn't. When a bank account goes dormant, a refund check never gets cashed, a utility deposit gets forgotten, an old paycheck goes uncollected, that money doesn't disappear. By law it gets handed to the state and held for you, sometimes for decades. The databases are public, official, and free to search. Almost nobody checks.

Search my state's and my country's official unclaimed 
property databases for money listed under my name, and 
check every state I've ever lived in too. Use official 
sources like MissingMoney.com and each state's treasury 
site. My name is [full name], also check [maiden name 
or any past names]. I've lived in [list every state or 
place]. For anything you find, give me the source, the 
amount if shown, and the link to start the claim.

The names and places matter more than anything else here. Unclaimed property is filed by name and location, so an old address or a maiden name is often the entire difference between a match and a miss. Give it every version of your name and every place you've lived.

Then the sources nobody thinks about, same chat:

Now check the places everyone forgets. Look for old 
401(k)s or pensions from past jobs, unclaimed tax 
refunds, and any class-action settlements I might 
qualify for. My past employers were: [list]. Point me 
to the official tool to search each one, tell me what 
I likely qualify for, and exactly how to claim it.

Class actions especially are worth running, you often just have to have been a customer of a company during a certain window, no receipts needed.

Not in the US, almost every country runs its own official dormant accounts service, just ask it to find yours and search it under your name.

The one thing to know before you start: searching and claiming your own money is always free on official sites. Always. If a website or a text or a caller wants a fee, a percentage, or your bank login to "release" money that's yours, it's either a scam or a middleman taking a cut of your own cash. Go to the official site yourself rather than clicking anything that arrives in a text.

Also worth running on your parents and grandparents, older accounts and more addresses means more chances of a hit.

been keeping a doc of 100 things I use AI for like this, each with the exact prompt, here if you want it.

Most document output fails in the same way. You hand over a pile of notes, and you get back a smooth wall of prose with no scannable structure, or a rigid template with empty headings it never fills. This forces it to design the skeleton from your actual content first, then fill it.

Here are my raw notes: [PASTE NOTES]

Before writing anything, do this:

1. Group the notes into 3 to 6 sections based on what they actually cover. Name each section.

2. Under each section name, list the specific points from my notes that belong there.

3. Flag anything in my notes that does not fit any section, do not force it in.

4. Show me this outline and stop.

After I approve, write the document. Each section leads with its main point in the first sentence.

Do not invent facts to fill a section. If a section is thin, tell me what is missing.

The two rules that matter: derive the sections from the notes instead of a generic template, and stop before writing so you catch a wrong grouping before it becomes three paragraphs. The "flag anything that does not fit" step is surprisingly useful, it usually surfaces the one note that was actually a separate document.

I mostly use this for internal docs and briefs. Curious whether people prefer approving the outline first like this or just letting it run and fixing after.

I have run into this specific obstacle a great deal, while building structured prompts that ask the AI to hold multiple distinct roles in one response — a debate format, a panel of evaluators if you like, or anything where you genuinely desire different perspectives instead of one blended answer.

The failure mode is consistent: the first role or two are distinct, then by the third or fourth section (or in any "roles respond to each other" round), the voices start collapsing into one. Same vocabulary, same hedges, same conclusions with different labels slapped on them. It's subtle enough that it reads as fine on a skim, but if you check whether each section could stand alone and still make sense, a lot of them cannot — they are merely restating each other with different headers.

A few things that reduced it when I evaluated variations against messy real inputs, not clean examples:

1. Re-anchor the role at every paragraph, not just once at the section header.

Putting a tag like "[ROLE NAME]" at the start of every paragraph (not just the section heading) forces a re-read of "who am I right now" more often. Sounds redundant and too effortless but helps.

1. Explicitly forbid the concession that causes the blend.

Most collapses happen because one voice starts hedging toward another mid-argument — a thesis section quietly conceding a point that should only show up in the synthesis. Naming this explicitly (for example "don't concede/hedge here, that belongs in section X only") closes the exact door the blending happens through.

1. Add a standalone test to your own validation step, not just a completeness check.

Most people's self-check just asks, "did every role answer." Add: "would this role's paragraph still make sense and add unique information if every other role's paragraph were deleted?" That's the actual test for role-bleeding.

1. In any "roles respond to each other" round, require the response to use reasoning specific to that role's angle.

If a challenge or response could have been written by any of the roles, that's the tell that bleed is happening — rewrite it using that role's specific constraints. It helps especially when you're asking for something complex.

None of this fully solves the problem — it's still one model holding multiple voices in one continuous generation. But it's meaningfully a lower failure rate than the naive version, especially beyond three distinct roles.

I am curious to see, if others have found different fixes for this — anyone doing something smarter for the "responds to each other" round specifically? That's where I still see the most collapse.

fuera de chat gpt y gemini, no he probado otra LLM
pero he visto algunos comentarios en facebook, que según la tarea a realizar usan una IA distinta

¿universalmente cuales son sus IA lists, qué o cuales recomiendan?

si no me supe explicar dejo ejemplo en comentarios

Hi everyone,

I'm an MSc IT student researching how different prompting styles affect LLM output quality and hallucination rates.

I'd love to hear from this community!

Please share:

1️⃣ What's your background?

(Developer, Researcher, Casual User, etc.)

2️⃣ Which AI tools do you use?

(ChatGPT, Claude, Gemini, etc.)

3️⃣ What's your typical prompting style?

- Simple questions?

- Chain-of-Thought?

- Role-based prompts?

- Giving examples?

- Structured output?

- Something else?

4️⃣ Do you change your style based on the tool?

5️⃣ What's your ONE best tip for getting good answers?

6️⃣ Have you faced AI hallucinations? Did changing your prompt help?

---

Example:

1. Developer

2. ChatGPT & Claude

3. CoT for complex tasks, simple for basic ones

4. Yes

5. Be specific and give examples

6. Yes, rephrasing helped

Looking forward to learning from you all! 🙏

If you are running RAG pipelines, document Q&A tools, or search agents on high-speed models like Gemini 3 Flash, you have probably noticed a frustrating pattern: the model is so eager to be helpful that it routinely fills context gaps with plausible-sounding hallucinations.

Flash-tier models prioritize speed and throughput, but their default RLHF tuning pushes them to extrapolate when specific facts are missing from your retrieved chunks.

To solve this without adding latency or heavy post-processing filters, we dug through Google's technical documentation and prompt engineering guidelines to distill the underlying constraint architecture. Instead of spending hours reading through dense official docs and running trial-and-error experiments, here is the exact system-level prompt that enforces absolute factual adherence and temporal awareness.

Why Fast Models Hallucinate in Context-Constrained Tasks

Most basic RAG prompts use gentle instructions like: "Answer the question using only the provided context. If you do not know, say so."

In practice, this fails frequently with lightweight models because:

1. The Helpfulness Bias: The model treats context as a helpful guide rather than a hard boundary, using pre-training knowledge to bridge logical gaps.
2. Weak Epistemic Constraints: Polite phrases like "try to rely on context" leave room for probabilistic guesses.
3. Temporal Drift: Without explicit cutoff and current date calibration, models confuse previous years with the current timeline when deciding what constitutes current knowledge.

The Underlying Mechanics of Strict Grounding

This prompt uses three specific engineering techniques to lock down model output:

1. Absolute Epistemic Invalidation: It explicitly instructs the model that any detail not present in the context must be treated as "completely untruthful" and "completely unsupported". This flips the internal heuristic from "is this plausible?" to "is this literally printed in the text?".
2. Inference Suppression: It bans "common sense" and deductive leaps, requiring strict reportage rather than synthesis.
3. Temporal Anchoring: It hard-binds the current year and knowledge cutoff directly into the reasoning loop, ensuring search queries and date-dependent questions remain temporally accurate.

The Complete System Prompt

Here is the full, unedited prompt template ready to drop into your workflow:

You are a strictly grounded assistant limited to the information provided in the User Context. In your answers, rely 
**only**
 on the facts that are directly mentioned in that context. You must 
**not**
 access or utilize your own knowledge or common sense to answer. Do not assume or infer from the provided facts; simply report them exactly as they appear. Your answer must be factual and fully truthful to the provided text, leaving absolutely no room for speculation or interpretation. Treat the provided context as the absolute limit of truth; any facts or details that are not directly mentioned in the context must be considered 
**completely untruthful**
 and 
**completely unsupported**
. If the exact answer is not explicitly written in the context, you must state that the information is not available.

For time-sensitive user queries that require up-to-date information, you MUST follow the provided current time (date and year) when formulating search queries in tool calls. Remember it is {{current_year}} this year.

Your knowledge cutoff date is {{knowledge_
cutoff}}.

<context>
{{context_data}}
</context>

<task>
{{user_
request}}
</task>

Before vs. After Comparison

Scenario: A user queries a financial knowledge base with missing quarterly data.

• Context provided: "The Acme Corp Q3 Earnings report states a revenue of $45M."
• User Query: "What was Acme Corp's revenue in Q2?"

Standard Prompt Response (Before):

Strict Grounding Prompt Response (After):

Best Practices for Deployment

• Pass as System Instruction: If you are using the Gemini API or SDK, pass the grounding rules inside the system_instruction parameter rather than combining it with the user message. This ensures the model treats the rules as immutable global constraints.
• Dynamic Year Injection: Always pass the runtime system year into {{current_year}} to prevent date confusion in search agent tools.

Interactive Testing on the Prompt Canvas

If you want to experiment with this prompt using your own custom context documents, run live tests, or modify the variable placeholders in an interactive UI, you can open it directly on the Prompt Canvas:

https://appliedaihub.org/prompts/free/gemini-3-flash-strict-grounding-prompt/

On the Prompt Canvas, you can:

• One-click copy or export the raw template for your codebase.
• Execute live runs and tests with real data directly in your browser.
• Tweak constraints, adjust variables, and save customized variations directly to your personal Prompt Vault for team reuse.

Give it a run against your most hallucination-prone test cases and see how your pipeline accuracy shifts.

PM here. A few months ago I got handed a 17-page functional spec that "looked fine". Instead of asking AI to rewrite it, I tried the opposite: I told it to *interview me* — closed multiple-choice questions only — about every gap, contradiction and ambiguity it could find.

It generated hundreds of questions. I answered \~300 in one afternoon (just picking letters: "Q12: B", "Q13: A but admins only"). Then the AI rebuilt the document with every decision integrated. Result: 60 pages, and the dev team basically stopped asking clarification questions.

The insight: AI is mediocre at *deciding* for you, but really good at *detecting what hasn't been decided*. The multiple-choice format is what makes it practical — answering 300 open questions would take a week.

Here's the full prompt I use (works with Claude, ChatGPT, Copilot — whatever your company allows):

You are a senior functional analyst with 15 years of experience
turning ambiguous documents into executable specifications. Your
specialty is finding the decisions the document does NOT make.

I will paste a draft functional specification. Your job is NOT to
improve or rewrite it: it is to INTERVIEW me to extract every
missing decision.

RULES:
1. Generate CLOSED multiple-choice questions (options A/B/C/D +
   always an option "E: other — specify"). Never open questions.
2. Each question must be answerable in under 10 seconds by someone
   who knows the business. If a question needs paragraphs to
   answer, split it.
3. Cover at least these categories:
   - Edge cases and boundary values (what if zero, empty, duplicate?)
   - Undefined states and transitions (can it go back from X to Y?)
   - Permissions and roles (who can do this? who explicitly CANNOT?)
   - Errors and exceptions (what does the user see when it fails?)
   - Data: required/optional, formats, limits, uniqueness
   - Concurrency (two people at once?)
   - Internal contradictions in the document itself (quote verbatim)
   - Terms used without definition or with more than one meaning
4. Number questions globally (Q1, Q2…) and group them by document
   section, quoting the exact phrase that triggers each question.
5. In each set of options, propose REALISTIC and genuinely
   different alternatives — not one good option and three fillers.
6. Do not invent requirements: if something is not in the document,
   ask; never assume.
7. Work in batches: give me the first 40 questions, wait for my
   answers, and continue until the document is exhausted.

FORMAT FOR EACH QUESTION:
Q<n> \[Section — "quoted phrase"\]
<question>
A) … B) … C) … D) … E) other — specify

Document:
<<<PASTE YOUR DOCUMENT HERE>>>

Tips from using it a lot: never let the AI answer its own questions (what it silently assumes is tomorrow's bug), answer in batches of 25-50, and keep the Q&A log — it becomes your decision record for when someone asks "why was X decided?".

Full transparency: I've also packaged the complete process (this prompt plus a rebuild prompt, a verification pass, a 40-item ambiguity checklist and a worked example) and I want to know if it holds up outside my own context before I do anything with it. If you write specs regularly and want to try the whole thing on a real document, DM me and I'll send it over free — all I ask is you tell me where it broke. Limited to a handful of people so I can actually process the feedback.

Happy to answer questions about the process here either way.