xiry.

AI · Tip

8 Ways to Improve RAG Applications

Practical architectural strategies to fix common failure modes in RAG systems

From the Xiry library · Updated 3 October 2026

This guide outlines eight targeted strategies to solve common retrieval and preprocessing problems in naive RAG pipelines. It addresses issues like missed keywords, fragmented context, unindexed diagrams, and weak relevance ranking. Implementing these patterns helps developers build more accurate search and generation workflows.

How it works

8 architectural fixes for common RAG failure modes:

  1. Metadata Filtering: When queries contain specific constraints (such as years, dates, authors, or departments), pure semantic search often returns outdated documents. Use metadata tags to pre-filter or post-filter retrieved candidates.
  2. Semantic Chunking: Arbitrary fixed-length or character chunking often splits critical context across chunks (e.g., separating mission from vision). Use semantic chunking based on document structure or sentence similarity transitions to keep related concepts together.
  3. Vision Language Models (VLMs) for Images/Diagrams: Naive text extraction ignores charts, infographics, and diagrams. Run images through a VLM to generate descriptive captions and tabular data that can be embedded and searched.
  4. Query Decomposition: Complex queries that ask for multiple comparisons or multi-year shifts overload single-vector search. Break complex prompts down into smaller sub-queries, retrieve context for each, and combine them for generation.
  5. Better Embeddings: When retrieval fails to capture semantic meaning or nuanced domain terminology, upgrade to a more modern or fine-tuned embedding model.
  6. Lexical Search (BM25 / Hybrid Search): Dense embeddings struggle with exact phrase matching, acronyms, and niche keywords (e.g., specific internal project names). Combine dense vector search with sparse lexical search (BM25) into a hybrid retrieval pipeline.
  7. Add a Reranker: Vector similarity search excels at recall but can place irrelevant chunks in top positions. Pass the top retrieval results through a cross-encoder reranker to score and re-sort chunks by direct relevance to the query.
  8. Instruction-Following Reranker: When a query includes subjective or instructional constraints (e.g., 'prioritize recent achievements' or 'focus on security'), use an instruction-aware reranker that adjusts rank ordering according to the user's specific guidance.

What it's for

  • Fix inaccurate answers in enterprise search caused by arbitrary text chunking
  • Enable retrieval over visual data like charts and diagrams using VLMs
  • Combine BM25 and vector search to handle exact acronyms and product names
  • Improve precision by placing a cross-encoder reranker downstream of initial vector search

Key points

  • Metadata Filtering: Pre-filter or post-filter retrieved items by date, author, or tags
  • Semantic Chunking: Split documents based on structural boundaries rather than fixed token limits
  • VLM Captioning: Convert images and tables into searchable text descriptions
  • Query Decomposition: Split multi-part questions into targeted sub-queries
  • Model Upgrades: Switch to modern or fine-tuned domain-specific embeddings
  • Hybrid Search: Pair dense vector retrieval with sparse lexical BM25 matching
  • Reranking: Reorder top retrieval results with a cross-encoder for precision
  • Instruction-Following Rerankers: Adapt chunk relevance based on explicit query directions

Pricing

Free

Details

Type
Tip
Category
AI
Topic
RAG Techniques
Platforms
other
For
AI engineers and software developers building RAG systems
Tags
rag, retrieval, llm, embeddings, reranking, hybrid search

Why it was saved

  • @rajistics, 9 May 2025: Standard naive RAG frequently fails on dates, exact jargon, charts, multi-part questions, and semantic boundary splits; these techniques diagnose and solve each failure type.

Related

Updated