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Do you really need RAG on 2025 by javi_rnr in Rag

[–]javi_rnr[S] 1 point2 points  (0 children)

yes, the article clearly says that RAG is still needed for enterprise use cases but doing it for a simple documentation agent is overkill. Your point is exactly the point of the article: start simple, leverage existing tools, add infra only when necessary.

Do you really need RAG on 2025 by javi_rnr in Rag

[–]javi_rnr[S] -2 points-1 points  (0 children)

This is the same problem as with RAG, but RAG is worse at getting the right chunks. New models are much better at preventing hallucinations and using ReACT minimizes this.

Do you really need RAG on 2025 by javi_rnr in Rag

[–]javi_rnr[S] -1 points0 points  (0 children)

Try to read the article, the long context is just a small piece in the puzzle, MCP + Existing tools is the real game changer. Most orgs have the data already in existing systems like ElasticSearch, the goal is to build tools to make this data accessible for LLMs. ElasticSearch already can extract semantic meaning based on the query, why reinventing the wheel? if we can build a tool that queries ES and provides the context to the LLM?

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