This is a summary of the full tutorial published on howtostartprogramming.in .

TL;DR – Optimal Document Chunking for RAG in 2026 Why chunking matters : RAG models retrieve and generate based on context windows. Proper chunking maximizes relevance, reduces hallucinations, and fits within token limits of modern LLMs (8‑32 k tokens in 2026). Goal Metric Impact Typical Token Range (2026) Retrieval precision ↑ Recall & ↓ Noise 200‑800 tokens Generation fidelity ↑ Coherence & ↓ Hallucination 400‑1200 tokens Cost efficiency ↓ API tokens × price Variable (depends on model) Top chunking strategies for 2026 (choose one or combine): Semantic Sliding Window – overlap = 15 % of chunk size; uses embeddings to keep topic continuity. Adaptive Token Budgeting – dynamically size chunks based on per‑section token density (e.g., tables vs. prose).…