9 RAG Techniques That Actually Improve Retrieval Quality

Retrieval-Augmented Generation (RAG) is often described as a simple pipeline:

Query → Retrieve documents → Send context to an LLM → Generate answer

In production, however, retrieval is rarely that simple.

The retriever can return irrelevant documents. Important information may be buried in the middle of a document. A query may be too vague for semantic search. Retrieved chunks may lose their surrounding context. And sometimes the model does not need retrieval at all.

The quality of a RAG system therefore depends heavily on how information is retrieved, filtered, ranked, compressed, and presented to the model.

9 RAG Techniques That Actually Improve Retrieval Quality

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