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.
a Local RAG Pipeline with ChromaDB and Sentence Transformers
Implement retrieval-augmented generation locally with ChromaDB, sentence-transformers embeddings, and LLM generation without external API dependencies
RAG Hybrid Search Pattern
Combine keyword (BM25) and semantic (vector) search to improve retrieval accuracy in RAG pipelines. Fuse ranked results using reciprocal rank fusion.
AI Data Preparation Checklist
Checklist for preparing data for LLM and RAG systems: data collection, cleaning, chunking, embedding, deduplication, PII removal, format validation, quality scoring, and indexing with metrics and thresholds.
AI RAG Evaluation Checklist
A checklist for evaluating RAG system quality: retrieval accuracy, generation faithfulness, context relevance, answer correctness, citation accuracy, latency, and end-to-end testing with metrics and thresholds.
Complete Guide to LLM Prompt Engineering
Write effective prompts for AI models. Covers prompt patterns, few-shot learning, chain-of-thought, RAG, system prompts, temperature tuning, function calling, and evaluation strategies.
Complete Guide to RAG in Production
Build production RAG systems. Covers chunking strategies, embedding models, vector stores, retrieval optimization, reranking, hybrid search, evaluation, and deployment patterns for reliable retrieval-augmented generation.
Vector Databases — AI/ML Embeddings and Similarity Search
A practical guide to vector databases: embeddings, similarity search, approximate nearest neighbors, and choosing between Pinecone, Weaviate, pgvector, and Chroma.