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The article explores how LLMs can enhance search pipelines through query rewriting, focusing on retrieval queries versus computational queries. It presents a template-based methodology where LLMs generate specific textual elements (keywords, synonyms, pseudo-answers) inserted into predefined Elasticsearch DSL templates, reducing output variability. Experiments using Claude 3.5 Sonnet on BEIR, MLDR, and MIRACL benchmarks compare lexical keyword enrichment prompts and pseudo-answer generation, with the best prompt yielding roughly +1 point NDCG@10 and recall gains. Concrete examples illustrate typo correction, noise reduction, and keyword augmentation.
  • main points
  • unique insights
  • practical applications
  • key topics
  • key insights
  • learning outcomes

  • • main points

    • 1
      Clear categorization of queries into retrieval and computational types with distinct handling strategies
    • 2
      Template-based design that constrains LLM output to make query rewriting more deterministic and controllable
    • 3
      Systematic prompt iteration with quantitative NDCG and recall comparisons across multiple benchmarks

  • • unique insights

    • 1
      Expanding the original query via a fixed DSL template plus LLM-generated text avoids the merge complexity and intent drift of full query rephrasing
    • 2
      Keyword boosting via a bool query with the original query in a must clause and LLM terms in should clauses, linearly combined with BM25 scores, preserves original intent while adding recall

  • • practical applications

    • Provides a reproducible recipe for integrating LLM-based query expansion into Elasticsearch lexical search, including concrete prompts, DSL structure, and evaluation methodology that practitioners can adapt for their own retrieval benchmarks.

  • • key topics

    • 1
      LLM-driven query rewriting for retrieval versus computational queries
    • 2
      Template-based query expansion using Elasticsearch DSL
    • 3
      Lexical keyword enrichment and pseudo-answer generation experiments

  • • key insights

    • 1
      Template-constrained LLM output that keeps query rewriting deterministic and avoids intent drift
    • 2
      Empirical comparison of three keyword-extraction prompt variants showing the impact of explicit instructions
    • 3
      Concrete failure and success examples (typo correction, noise reduction, augmentation) for practical calibration

  • • learning outcomes

    • 1
      Distinguish retrieval queries from computational queries and select an appropriate rewriting strategy for each
    • 2
      Design a template-based Elasticsearch bool query that incorporates LLM-generated keywords or pseudo-answers as should clauses
    • 3
      Evaluate query rewriting experiments using NDCG@10 and recall metrics with pytrec_eval
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     Original link: https://www.elastic.co/search-labs/blog/query-rewriting-llm-search-improve

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