Tired of Poor RAG Results?

If you are tired of poor RAG results then follow these steps, a coffee break concepts Vol 2

The most important thing to improve your RAG output is to improve your search.

To improve RAG results, first try to improve the search in RAG

Retrieval (Often called L1)

Goal of this step is to quickly find all the documents from the index that satisfy the search criteria -possibly across millions or billions of documents.

Retrieval Search in RAG

Ranking(Often called L2)

Takes a subset of the top L1 results and computes higher quality relevance scores to reorder the result set. The L2 can improve the L1’s ranking because it applies more computational power to each result.

But which one should you use?

Hybrid Search with semantic ranker outperforms every other search.

Hybrid Search with semantic ranker outperforms every other search.

Wait, how to write your query for vector search?

Different ways to write queries in RAG

But which one would work and which one won’t?

Hybrid search + semantic ranking consistently outperforms any type of search.

Hybrid search + semantic ranking consistently outperforms any type of search.

Your Document Chunking strategy matters

Document Chunking strategy matters

Number of tokens per vector & Chunk boundary

Number of tokens per vector & Chunk boundary

Summary

• Hybrid Search with semantic ranker outperforms every other search
• There are multiple styles of writing your query, always benchmark your query type against your dataset
• Its always preferred to vectorize your chunk instead of complete document
• Higher chunk size results in lower recall — it is important to check what chunk size works on your dataset
• Overlapping chunks helps improve understanding the context

Comment below on which topic you want to understand next in this “Coffee Break Concepts” series and we will include those topics in the upcoming weeks.

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