What RAG is, how it works, chunking, embeddings, retrieval, and citations. 18 articles, written by the engineers building AI App Lab.
RAG reranking: cross-encoders process query + document together for 10-20% retrieval improvement. Compare Cohere Rerank 3, BGE-Reranker-v2-m3, FlashRank, Jina, Voyage. Bi-encoder vs cross-encoder vs ColBERT trade-offs, latency benchmarks, and fine-tuning guide for production RAG.
Choose embedding model 2026: Cohere embed-v4 (65.2 MTEB), OpenAI text-3-large (64.6), BGE-M3 (63.0 open-source). Compare 10 models on dimensions, price per 1M tokens, context window, MTEB score, multilingual support, and deployment model. Decision framework and code examples.
Cosine similarity vs dot product: for L2-normalized vectors they are mathematically identical. Cosine is the safe default for text embeddings. Dot product is faster on normalized vectors. Normalize at ingest, search with inner product. Euclidean distance suffers curse of dimensionality. Code examples and decision framework.
RAG document indexing: parse PDF, DOCX, Excel with unstructured.io, PyMuPDF, python-docx, openpyxl. 7-stage pipeline: extract, classify, structure, chunk, enrich, embed, validate. SHA256 dedup, metadata extraction, table preservation, OCR fallback. Code examples and production checklist.
RAG vs fine-tuning vs prompting: decision framework from 200 enterprise deployments. RAG costs 60-80% less than fine-tuning, 78% eventually outgrow prompt-only, fine-tuning is for style not facts. 6-factor decision matrix, hybrid stacks, and distillation strategy for 2026.
How RAG works step by step: 7 stages from document ingestion to answer generation — collect, chunk, embed, store, retrieve, rerank, generate. Covers indexing pipeline, hybrid search, cross-encoder reranking, grounded prompts, and evaluation with code examples.
Prevent RAG hallucinations: grounded prompting with citations, refusal behavior, context-only answers, cross-encoder reranking, hybrid search, RAGAS faithfulness evaluation, confidence thresholds. 10 techniques with code examples, before/after benchmarks, and production checklist.
Multimodal RAG: ColPali, CLIP, and vision-language models for retrieval over images, charts, tables, and text. Dual-encoder architecture, cross-modal fusion, ViDoRe benchmarks. Compare ColPali multi-vector vs single-vector (Cohere Embed 4, Voyage multimodal). Code examples and production patterns.
RAG citations implementation: inline citations with source labels, page numbers, and bounding boxes. Fine-grained citation methods, provenance tracking, citation verification, and UI rendering. Compare chunk-level vs sentence-level vs span-level citations with code examples and production patterns.
What are embeddings: dense vectors of 384-3072 numbers where proximity equals semantic similarity. Transformer encoders, tokenization, pooling, L2 normalization, cosine similarity. From Word2Vec to 2026 instruction-tuned models. Code examples, dimensionality guide, and production practices.
RAG chunking strategies compared: recursive character splitting at 512 tokens wins 69% accuracy, fixed-size 67%, semantic chunking requires careful tuning. 7 strategies benchmarked with recall, F1, groundedness. Decision framework, code examples, and overlap guidance for production RAG.
RAG conversation memory: query rewriting for contextual retrieval, sliding window memory, summary-based compression, and long-term persistent memory. Compare 5 memory strategies with token budgets, latency, and coherence. Code examples for multi-turn RAG with session management and contextual awareness.
RAG evaluation metrics: RAGAS faithfulness (target ≥0.90), answer relevancy (≥0.85), context precision (≥0.80), context recall (≥0.85). Plus recall@k, nDCG, groundedness. Compare RAGAS, DeepEval, TruLens frameworks. Code examples, threshold tables, and CI integration for production RAG.
Build a RAG pipeline from scratch in Python: FastAPI backend, Qdrant vector database, text-embedding-3-small, hybrid search with BM25, cross-encoder reranking, grounded prompts with citations, RAGAS evaluation, and streaming responses. Full production code.
What is RAG: retrieval-augmented generation connects LLMs to external knowledge bases via embeddings, vector databases, hybrid search, and cross-encoder reranking. Developer guide covering indexing pipeline, retrieval pipeline, chunking, and production architecture.
RAG chunk size: 256-512 tokens for factoid queries, 512-1024 for analytical. NVIDIA benchmarks show 0.648 accuracy at page-level. Getting chunk size wrong by one bracket degrades context precision by 15-30%. Sweep 256, 512, 1024 on your data with code examples and decision framework.
LangChain vs LlamaIndex for RAG: LangChain excels at orchestration, agents, LangGraph stateful workflows, LangSmith observability. LlamaIndex excels at retrieval, indexing, LlamaParse, query transformation. Compare strengths, code volume, hybrid pattern. Decision matrix with 6 criteria and production checklist.
Hybrid search for RAG: BM25 + dense vectors + Reciprocal Rank Fusion pushes recall from 0.72 to 0.91. RRF uses ranks not scores, solving score incompatibility. Qdrant native hybrid, LangChain EnsembleRetriever, per-branch weighting. 4-stage pipeline with code examples and latency benchmarks.