RAG Enhancement: Chunking Optimization for Improved Retrieval and Context

[cloudbyday90]

Overview

Optimize the chunking and embedding strategy used by the RAG system (Retrieval Augmented Generation) to improve retrieval accuracy and context density.

Why?

• Well-chosen chunk sizes and strategies increase recall and reduce irrelevant retrievals
• Avoids missing relevant information when context is cut off by chunk boundaries
• Aligns with 2025–2026 industry best practices (Reference 2)

Implementation Plan

1. Backend (Node/Express)
   • Audit all code producing embeddings for classification_history.
   • Experiment with different chunk sizes (e.g., 256, 512, 1024 tokens) and overlaps.
   • Benchmark chunking strategies using real-world classification history:
     • Field-based (title, genres, overview, studio)
     • Sentence or windowed document chunking (sliding window)
   • Update embeddingService.formatForEmbedding() and related data generators.
   • Consider late fusion (retrieve on each field, then combine results) or weighted concatenation.
   • Example code adjustment:

   // Inside embeddingService.formatForEmbedding()
   const combined = [metadata.title, metadata.genres, metadata.overview].join(' ');
   // Optionally, use weights per field or windowed slices

   • Expose chunking parameters in AI settings for admin tuning.
2. QA & Validation
   • Design experiments measuring retrieval precision/recall as chunk size/overlap is modified
   • Document best settings for your media collection size/type

References

• Collabnix: RAG Guide 2025

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