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65 points appwiz | 1 comments | | HN request time: 0.819s | source
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imiric ◴[] No.44382347[source]
This is great. We need more research into solving this fundamental problem, yet AI companies prefer to chase benchmarks and pump out value-added products.

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.

replies(1): >>44383922 #
1. nijave ◴[] No.44383922[source]
I think there's room for more agentic systems that combine RAG, MCP and traditional static analyzer tools

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.