RAG Tutorial
Step-by-step retrieval-augmented generation tutorials with enterprise patterns and implementations.
Continue on the dedicated site below—this portal keeps enterprise reference content alongside that curriculum.
Open RAG Tutorial →00Introduction
Overview
Retrieval-Augmented Generation augments LLM prompts with externally retrieved evidence, grounding answers in enterprise or fresh data instead of parametric memory alone.
Technical Deep Dive
Formal flow: q → Retriever(D) → {d₁…dₖ} → Prompt(q, docs) → LLM → a. Reduces factual hallucination when retrieval recall is high. Bottleneck is usually retrieval quality, not generation.
Architectures: naive RAG, advanced (rerank, hybrid), modular (query router), agentic (iterative retrieve).
Practical Use Case
Support bot indexes 8k Confluence pages; answers include doc links; escalations drop 30% when retrieval MRR@5 > 0.85.
01Why RAG
Overview
RAG solves knowledge staleness, proprietary data access, and attribution requirements that pure fine-tuning cannot address cost-effectively.
Technical Deep Dive
Fine-tuning embeds knowledge in weights (expensive to update, opaque citations). RAG updates by re-indexing documents. Hybrid: RAG + light fine-tuning for tone/format.
When NOT to use RAG: tasks needing pure reasoning without external facts, ultra-low latency (<200ms), or when all knowledge fits reliably in context.
Practical Use Case
Pharma company uses RAG over clinical trial PDFs (updated weekly); fine-tuning only for regulatory writing style—avoids retraining 70B model per study release.
Chunking Strategies
Overview
Chunking splits documents into retrieval units; strategy directly impacts recall—too large dilutes relevance signals, too small loses coherence.
Technical Deep Dive
Fixed-size: 512 tokens, 10–20% overlap. Semantic: split at embedding discontinuities. Structure-aware: by heading, paragraph, table row. Parent-child: retrieve small, return large parent for context.
Tables need special handling (markdown/HTML preservation). Code: chunk by function/class.
Practical Use Case
API documentation chunked by endpoint (child) with section overview (parent)—developers get full endpoint page when any child matches query.
Vector Databases
Overview
Vector databases optimize approximate nearest neighbor (ANN) search over high-dimensional embeddings at million-to-billion scale with metadata filtering.
Technical Deep Dive
Algorithms: HNSW (graph-based, low latency), IVF (inverted files, memory efficient). Metrics: cosine, dot product, L2—match your embedding model training.
Compare: FAISS (library), Chroma (embedded), Qdrant/Weaviate/Milvus (distributed, filtering). Evaluate recall@k, QPS, filtering on tenant_id at 99th percentile latency.
Practical Use Case
Multi-tenant SaaS uses Qdrant with payload index on org_id; 50M vectors, p99 search 45ms, hybrid with Elasticsearch for SKU exact match.
Hybrid Search
Overview
Hybrid search fuses dense vector similarity with sparse lexical matching (BM25) to capture both semantic paraphrase and exact identifiers (SKUs, error codes, statutes).
Technical Deep Dive
Fusion: Reciprocal Rank Fusion (RRF), weighted linear combo, or cascade (BM25 filter → vector rerank). Critical for enterprise docs with codes, names, numbers.
Tune weights per corpus: legal → higher BM25; conversational FAQ → higher dense.
Practical Use Case
IT helpdesk RAG: Windows error code 0x80070005 retrieved via BM25; symptom description via vectors—RRF combines both; resolution rate +18%.
Agentic RAG
Overview
Agentic RAG lets an LLM controller decide when to retrieve, rewrite queries, choose indexes, or iterate—handling multi-hop questions single-shot RAG misses.
Technical Deep Dive
Patterns: Self-RAG (reflect on relevance), corrective RAG (retry if low confidence), multi-index router. State tracks prior retrieval results to avoid duplicate search.
Costs more LLM calls—justify for analyst/research tiers not high-volume FAQ.
Practical Use Case
Competitive intelligence agent runs 3 retrieval rounds: broad industry scan → competitor filter → financial filing deep dive—synthesizes board-ready brief.