Use a decision-tree approach to choose a chunking strategy for your LLM applications.
A six-question decision tree for choosing retrieval-augmented generation chunking strategies by structure, cost, needs, and failure signals.
retrieval-augmented generation
The Chunking Paradox: Why RAG Breaks Down Before Retrieval Begins
RAG failures often start at ingestion: bad chunk sizes split facts and hide context. Balanced chunks are key to accurate retrieval.
Advanced retrieval-augmented generation chunking involves hierarchical, semantic, and contextual retrieval.
Fixed chunking fails on messy documents; semantic, parent-document, breadcrumbs, and contextual strategies improve retrieval and generation.
Creating a Retrieval-Augmented Generation Pipeline Using LLM Embeddings and Scikit-learn
Build a free local retrieval-augmented LLM pipeline with scikit-learn, embeddings, and Qwen2.5-0.5B, covering chunking, cosine retrieval, generation.
From RAG to Reliability: Building Genuinely Truthful AI Harnesses
Retrieval-augmented generation alone cannot prove claims. A truthful AI harness treats retrieved passages as candidates, not evidence, and verifies each factual statement with inspectable sources, cla…