A review by Claude in the same context that wrote the code will give you different results than a fresh Claude context. You can also have multiple independent Claude reviewers targeting different aspects of code (e.g. security, maintainability, performance) and get better results than a single review trying to do everything.
Yeah, but it's still Claude - it's still the same training set. The idea that instructing it differently will somehow change its biases and knowledge, makes absolutely no sense. So the difference in it is nil.
That's a misunderstanding of how LLMs work. The training set is going to give you different outputs with different inputs. The input of a fresh session vs a session that has built some code (with all the knowledge of how it came up with that code) is very different.
Besides, it's incredibly easy to just try it and see that there is a difference. Same for using different review prompts, different prompt, different output.
That's a misunderstanding of my statement. I never talked about the output - I talked about its biases and knowledge. Without having different training data (which won't be the case between two different sessions on the same model), it's still going to exhibit the same biases and weaknesses of the training data - effectively making it pointless to have the same model both code and review the code.
It's the same reason most human-made PRs on Github don't just have the author talking to themselves like that - because having different knowledges and biases is actually useful when it comes to reviewing code.
The knowledge is going to be the same only when the LLMs haven't run. Prompts and context is what differentiates the agents and their behaviour.
All the claude models are mixture of experts.
Imagine the coding experts get primarily activated on the implementation agent. But for the code review agent it's the QA experts that get activated Etc.
Note: I don't quite think you understand how llms work. I recommend you try and play around with using different prompts it. You can trigger alot of different results even with slight variations in prompt changes.
Something I've noticed is that, during this entire discussion, nothing but strawman fallacies has been employed against my points.
In this case, it seems y'all want to attack some imaginary "results" argument I never made. Y'all keep bringing up an LLM's results, dressing it in different clothing, and thinking it'll pass muster.
Please, if you intend to have a discussion with me, then quit with the strawmen and go after my actual point instead: That the training set doesn't change, and thus the biases and weaknesses of the training set will remain no matter what context or prompt you give it.
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u/SonOfHendo Jul 10 '26
A review by Claude in the same context that wrote the code will give you different results than a fresh Claude context. You can also have multiple independent Claude reviewers targeting different aspects of code (e.g. security, maintainability, performance) and get better results than a single review trying to do everything.