Topic
Chunking Strategies for RAG
4 posts on chunking strategies for rag — part of glossary on the n4n AI blog.
GlossaryHow-to
What chunk size should you use for RAG embeddings
A step-by-step guide to choosing and tuning chunk size for RAG embeddings, with runnable code for evaluation and production monitoring.
6 min read
GlossaryAnalysis
How chunk overlap affects RAG retrieval quality
How chunk overlap changes retrieval precision and recall in RAG systems, with concrete tuning guidance and code examples.
4 min read
GlossaryComparison
Fixed-size chunking vs semantic chunking for RAG
Compare fixed-size vs semantic chunking for RAG: trade-offs on retrieval quality, latency, cost, and implementation complexity with code examples.
5 min read
GlossaryGuide
Chunking strategies for PDFs and long documents
A practical guide to chunking PDFs for RAG — strategies, code patterns, and tradeoffs for production retrieval systems.
6 min read
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88