The RAG-based mitigation is interesting, but quite limited, as mentioned. It would only work if the user can provide ground truth data, which for code generation is relatively straightforward, but it's much more difficult for most other factual information. We can't directly rely on data from the web, since the sources need to be carefully reviewed by a human first, which is the labor-intensive task that requires human domain experts.
So this approach seems like a band-aid, and wouldn't be generally applicable. I'm not in the AI industry, but from the perspective of a user it seems that the hallucination problem requires a much more foundational solution.
For instance, RAG could be used to provide coding standards and best practices such as sanitizing user inputs used in file system lookups. MCP could be used to integrate with up to date and authoritative (official) docs. Static tools could run and analyze the results and feed errors back into the LLM to correct.
It seems a lot of tools rely on raw LLM queries and expect the IDE or other tools to take over instead of providing a consolidated experience.
I was surprised that this paper talked more about RAG solutions than tool-use based solutions. Those seem to me like a proven solution at this point.
The problem is that many hallucinations do not produce a runtime error, and can be very difficult to spot by a human, even if the code is thoroughly reviewed, which in many cases doesn't happen. These can introduce security issues, do completely different things from what the user asked (or didn't ask), do things inefficiently, ignore conventions and language idioms, or just be dead code.
For runtime errors, feeding them back to the LLM, as you say, might fix it. But even in those cases, the produced "fix" can often contain more hallucinations. I don't use agents, but I've often experienced the loop of pasting the error back to the LLM, only to get a confident yet non-working response using hallucinated APIs.
So this problem is not something external tools can solve, and requires a much deeper solution. RAG might be a good initial attempt, but I suspect an architectural solution will be needed to address the root cause. This is important because hallucination is a general problem, and doesn't affect just code generation.
I define hallucinations as a a particular class of mistakes where the LLM invents eg a function or method that does not exist. Those are solved by ensuring the code runs. I wrote more about that here: https://simonwillison.net/2025/Mar/2/hallucinations-in-code/
Even beyond that more narrow definition of a hallucination, tool use is relevant to general mistakes made by an LLM. The new Phoenix.new coding agent actively tests the web applications it is writing using a headless browser, for example: https://simonwillison.net/2025/Jun/23/phoenix-new/
The more tools like this come into play, the less concern I have about the big black box of matrices occasionally hallucinating up some code that is broken in obvious or subtle ways.
It's still on us as the end users to confirm that the code written for us actually does the job we set out to solve. I'm fine with that too.
Don't hallucinations mean nonexistent things, that is, in the case of code: functions, classes, etc. How could they fail to lead to a runtime error, then? The fact that LLMs can produce unreliable or inefficient code is a different problem, isn't it?
Tell these our Python developers who don't test anything outside of a narrow happy path.
To be fair this is also how I generally try to write IAM configs without AI.
I suspect it's just like with humans. People who learn quickly and don't carefully curate their knowledge to resolve contradictions as they learn, they tend to make similar mistakes when it comes to subjects which they did not invest much time fully studying.
If I was an AI researcher, what I would try to do is find the highest quality information possible concerning very few axiomatic topics, with as few contradictions as possible, then train it into the LLM until it can generate text and basic reasoning which is fully accurate... Then once we have this basic but fully rational AI, start feeding it new data but, before giving it any piece of data to learn from, you first ask the AI to indicate if this new data contradicts any of its current knowledge. You only let it update its weights with the new data as-is if it does not contradict its existing knowledge. If it does contradict its existing knowledge, either discard it or maybe feed it the data but with some synthetic preamble like "Some people believe that..." so that it's aware of the existence of this belief system but knows that it's not to be internalized as its own beliefs.
Or maybe there is a way to do this to detect contradictions by looking at the weights themselves. You can rollback a round of training if the weights update in a way which suggests that a conflicting piece of information was learned in a specific round of training. Maybe there can be a different ANN which looks at the weights of the LLM during training and it was trained to detect contradictions and decides when to rollback a round of training.
A simpler explanation, and I posit a correct one, is people anthropomorphize an algorithm by describing the result of a particular path within a statistical model used to generate tokens as being "hallucinations" due to them being unexpected by the person interpreting the text.
> I suspect it's just like with humans.
Therein lies the problem.
