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Matching Retrieval Strategy to Data Shape & Query Pattern

Core

Match retrieval strategy to data shape and query pattern · Difficulty 3/5

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retrieval-strategydata-shapehybrid-retrievalrag

Explanation

There is no single right retrieval design. The architect matches strategy to the data and the queries rather than reaching for a favorite technique on every project.

Strategy by Data Shape

  • Structured / tabular data with precise lookups → query the source of truth directly (SQL/API) rather than embedding it. Embedding a database is rarely the right move when the database can just be queried.
  • Exact identifiers, codes, names → lexical/BM25 shines; pure semantic search may miss exact matches on rare terms.
  • Natural-language, paraphrase-heavy questions → semantic embeddings, with reranking added for precision.
  • Mixed corpora → hybrid retrieval, combining embeddings and BM25.
  • Small, stable reference set that fits comfortably → it may be cheaper and simpler to place it in a cached prompt prefix than to build a full retrieval pipeline for it.

The Core Principle

The point is *fit*: retrieval strategy follows from what the data looks like and how users ask, not from a preferred technique applied uniformly. A production integration will often mix several of these strategies side by side — direct queries for structured data, hybrid retrieval for a mixed document corpus, and a cached prefix for a small stable glossary — rather than forcing everything through one pipeline.

Key Takeaways

  • Structured/tabular precise lookups should query the source of truth directly, not be embedded
  • Exact identifiers, codes, and names favor lexical/BM25; semantic search may miss them
  • Natural-language, paraphrase-heavy questions favor semantic embeddings plus reranking
  • Mixed corpora call for hybrid retrieval combining embeddings and BM25
  • A small, stable reference set may be cheaper placed in a cached prompt prefix than built into a retrieval pipeline

Glossary Terms

Related Concepts

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