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Naive RAG for Large KB

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Overview

Naive Retrieval-Augmented Generation (RAG) is the simplest approach for combining information retrieval with large language models (LLMs).

Typically, it involves straight forward indexing: chunking documents, embedding those chunks and storing them in a vector DB. For the query processing naive RAG implementations simply retrieve the top-k most similar pieces for the query from the vector DB. The retrieved chunks are then passed to the LLM as context for generation.

If you want to analyze large amounts of data, it's a bit like tearing out pages from different books and then laying them out in a jumbled mess to give a meaningful answer.

Drawbacks of Naive RAG for Large Knowledge Bases

While simple RAG pipelines are easy to implement and work reasonably well for small or moderately sized knowledge bases (KBs), they face significant limitations as the KB grows:

  • Retrieval Challenges The simple retrieval phase often struggles with precision and recall, leading to the selection of misaligned or irrelevant chunks, and the missing of crucial information.
  • Chunking Issues: Naive chunking can break semantic units, leading to incomplete or incoherent context. This reduces answer quality, especially for complex or multi-hop queries.
  • Lack of Reasoning/Ranking: Naive RAG does not re-rank or reason about the retrieved chunks beyond their embedding similarity. It cannot synthesize or cross-reference information across multiple documents effectively.

Why Naive RAG is Not Sufficient for Large Content

For large KBs, the limitations above often result in:

  • Missed or incomplete answers due to poor retrieval coverage.
  • Hallucinations or irrelevant output from the LLM.
  • Inefficient use of compute and memory resources.

Recommendations

  • Use advanced RAG or modular RAG: implement better retrieval strategies, smarter chunking (semantic segmentation, overlapping...)
  • Integrate feedback loops and evaluation mechanisms.

References & Further Reading