Topic
Retrieval-Augmented Generation (RAG) Basics
5 posts on retrieval-augmented generation (rag) basics — part of glossary on the n4n AI blog.
What is RAG? Retrieval-augmented generation explained
A practitioner's guide to retrieval-augmented generation — what RAG is, how the retrieval and generation stages work, why it matters for production LLM systems, and the misconceptions that trip up engineers.
The three stages of a RAG pipeline explained
A practical breakdown of the three RAG pipeline stages — retrieval, augmentation, and generation — with code patterns, common pitfalls, and tradeoffs engineers face in production.
RAG explained for non-technical readers
A clear, practical explanation of Retrieval-Augmented Generation for engineers — how it works, why it matters, a working example, and the misconceptions that trip up implementations.
How RAG reduces hallucinations in LLM answers
A technical analysis of how retrieval-augmented generation grounds LLM outputs in verifiable sources, with implementation patterns and honest failure modes.
How RAG grounds LLM answers in real documents
A step-by-step guide to building a RAG pipeline that grounds LLM answers in your documents, with runnable code for ingestion, retrieval, and cited generation.
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