Advanced RAG Improvements

Advanced RAG refers to systems that build upon the limitations of Naive RAG by optimizing both retrieval and generation using additional strategies.

Pre-Retrieval Enhancements

These are techniques applied before fetching documents:

  1. Query Rewriting

    • Rephrases or expands the user query to better match stored documents.

    • Example: Rewriting “LLM” → “large language model”.

  2. Query Expansion

    • Adds additional terms or synonyms to improve recall.

    • Useful in domain-specific vocabularies or ambiguous queries.

  3. Multi-query Retrieval

    • Splits the user query into several sub-queries, retrieves separately, and merges results.

    • Helps cover more ground and improve completeness.

  4. Use of Metadata Filters

    • Filters retrieval using metadata (e.g., document_type = "FAQ").

    • Increases precision by eliminating irrelevant sections.

Post-Retrieval Enhancements

These are applied after initial document retrieval, before feeding them to the generator:

  1. Reranking

    • Uses a secondary model to reorder retrieved documents by semantic relevance.

    • Can be done using cross-encoders or reranking LLMs.

  2. Context Compression

    • Reduces retrieved content using summarization or token pruning.

    • Helps fit more content within LLM token limits.

  3. Repacking and Ordering

    • Rearranges documents to improve logical flow.

    • Example: Grouping by source or time, sorting by importance.