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Advanced Retrieval

📚 Agentic RAG10 min80 BASE XP

Beyond Basic Vector Search

Simple vector search fails when concepts are spread out or use completely different vocabulary. Production systems use Hybrid Search.

  • Dense Search (Embeddings): Matches semantic meaning (e.g., "puppy" matches "dog").
  • Sparse Search (BM25/Keyword): Matches exact keywords (e.g., "CVE-2023-4521").

Reranking

Always fetch more documents than you need (e.g., top 20), then use a dedicated Reranker model (like Cohere Rerank) to resort them. The reranker is much more accurate but too slow to run on millions of documents, so it's used as a second pass.

SYNAPSE VERIFICATION
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What is the benefit of a Reranker in a RAG pipeline?
It generates the final answer
It compresses text into embeddings
It acts as a highly accurate second-pass sorter for documents initially fetched by a vector search
It translates text to other languages
Watch: 139x Rust Speedup
Advanced Retrieval | Agentic RAG — AI Agents Academy