Tag: rag
Browse 7 practical software engineering resources tagged with "rag". Discover code recipes, design patterns, documentation templates, and in-depth guides to help you build, deploy, and maintain production-ready solutions involving rag. Each resource is written for engineers who ship real systems, with copy-paste examples and practical trade-offs.
Retrieval-Augmented Generation
RAG combines language models with external knowledge retrieval to improve accuracy, reduce hallucinations, and ground responses in real data.
The resources below cover vector databases, embeddings, chunking strategies, retrieval pipelines, and evaluation. Each guide helps you build RAG systems that produce trustworthy answers.
Every resource includes clear explanations, copy-paste code, and practical warnings. Use them to make informed decisions, avoid production pitfalls, and speed up your delivery. If you are just getting started, read the beginner-friendly articles first; if you are experienced, jump straight to the advanced patterns and architecture guides. New resources are added regularly, so bookmark this page and check back for the latest patterns.
a Local RAG Pipeline with ChromaDB and Sentence Transformers
Implement retrieval-augmented generation locally with ChromaDB, sentence-transformers embeddings,...
RAG Hybrid Search Pattern
Combine keyword (BM25) and semantic (vector) search to improve retrieval accuracy in RAG pipelines....
AI Data Preparation Checklist
Checklist for preparing data for LLM and RAG systems: data collection, cleaning, chunking,...
AI RAG Evaluation Checklist
A checklist for evaluating RAG system quality: retrieval accuracy, generation faithfulness, context...
Complete Guide to LLM Prompt Engineering
Write effective prompts for AI models. Covers prompt patterns, few-shot learning, chain-of-thought,...
Complete Guide to RAG in Production
Build production RAG systems. Covers chunking strategies, embedding models, vector stores,...
Vector Databases — AI/ML Embeddings and Similarity Search
A practical guide to vector databases: embeddings, similarity search, approximate nearest...