RAG Chunking: Balancing Chunk Size for Precision and Context

Bashiri Smith · Facebook reel · 2026-09-04 · 0:29 · 6,322 views · Open on Facebook

Topics: Retrieval-Augmented Generation (RAG) · Level: beginner

Summary

The video explains how bad chunking can break a RAG pipeline. If chunks are too large, the right information is retrieved but buried in irrelevant context sent to the LLM. If chunks are too small, information the model needs gets split apart. No single chunk size works for every system, so you have to test chunking against your own documents, queries and retrieval results.

Key points

• Bad chunking is one of the easiest ways to ruin a RAG system.
• Chunks that are too large (e.g., a whole document as one chunk) may contain the right section, but they also send the LLM a lot of irrelevant information.
• Chunks that are too small can split apart context the model needs to understand the answer.
• Aim for a balance: small enough for precise retrieval, large enough to keep context.
• No single chunking strategy or chunk size fits every RAG system.
• Tune chunking by testing it against your actual documents, queries and retrieval results, and iterate.

Resources mentioned

• The Complete AI Engineer Roadmap for 2026 (Exact Courses + Step-by-Step) · video · youtube.com · free
  Bashiri Smith's free YouTube training on upgrading your skills and becoming competitive for AI engineering roles, with specific courses listed step by step.
  Also in: SWE-to-AI Engineer Plan for 2027: LLMs, RAG, Agents, Evals, Job Search (Bashiri Smith on Facebook · notes), Pointer to Bashiri Smith's Complete AI Engineer Roadmap for 2026 (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), Software Engineer to AI Engineer Before 2027: A 5-Step Career Plan (Bashiri Smith on Facebook · notes) and 19 more
• BASWE.Ai Engineer (Skool community) · community · skool.com · paid
  The creator's paid community and program, with an AI learning roadmap (including the full ops and evaluation track), daily calls with engineers and recruiters, resume and portfolio help, and a job-search pipeline.
  Also in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Pointer to Bashiri Smith's Complete AI Engineer Roadmap for 2026 (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How to Evaluate a RAG Pipeline: Retrieval vs. Generation (Interview Answer) (Bashiri Smith on Facebook · notes) and 76 more

Try this

• Choose chunk sizes that are small enough for precise retrieval but large enough to keep context.
• Experiment with chunking strategies and test them against your own documents, queries and retrieval results.
• Comment "chunk" on the video to get the link to the creator's AI engineer training/roadmap.