Retrieval-Augmented Generation

Build production RAG systems — embeddings, vector search, chunking, and evaluation.

RAG is the most practical AI system pattern today. If you can build retrieval, you can build AI products.

What you’ll be able to do

Build end-to-end RAG pipelines with vector search
Implement chunking strategies and embedding optimization
Design retrieval evaluation and quality metrics
Deploy production RAG systems with monitoring

Curriculum

Phase 1: Your First RAG System

Hands-on RAG pipeline from scratch. Postgres + pgvector, OpenAI embeddings, naive chunking. By the end you have a working demo.

Your First RAG System

Stand up Postgres + pgvector, generate OpenAI embeddings, ingest a corpus, run naive retrieval, and ship a working chat-with-docs demo end-to-end.

Phase 2: RAG Core Pillars

Embeddings, chunking, retrieval, generation — the four levers that decide whether your RAG works in production. Each one has tradeoffs and failure modes.

Embeddings & Vector Search

Embedding model tradeoffs (OpenAI vs BGE vs E5), cosine vs dot product, HNSW vs IVF index choice, dimensionality, and the ANN tuning that actually moves the needle in production.

Chunking Strategies

Fixed-size vs recursive vs semantic chunking, overlap design, table and code preservation, header-aware splits, and the A/B benchmark methodology to pick a default that survives your corpus.

Retrieval Optimization

Hybrid retrieval — BM25 + dense fused via Reciprocal Rank Fusion — cross-encoder reranking, score floors, query rewriting, and the latency-budget tradeoffs every reranker buys you.

Generation & Prompting

Context assembly under retrieval, citation injection, system-prompt design for grounded answers, streaming, and hallucination guards that don't kill answer quality.

Phase 3: Production RAG

Evaluation, advanced patterns, and deployment. Recall@k, hallucination detection, query routing, and the observability you need to keep RAG alive on-call.

RAG Evaluation

Recall@k, MRR, RAGAS faithfulness / answer-relevancy / context-precision / context-recall, golden-set design, and eval-as-canary in CI so retrieval regressions get caught before users do.

Advanced RAG Patterns

Query expansion, multi-query, HyDE, multi-vector, ColBERT-style late interaction — plus the RAG security surface: prompt injection through documents, retrieval leakage, and access-control patterns.

Production Deployment

Caching strategies (semantic + exact-match), cost attribution per query, monitoring (latency, hit rate, faithfulness drift), runbook design, and the observability stack that keeps RAG alive on-call.

What you’ll build

Document chunking and embedding pipeline
Hybrid retrieval service (semantic + keyword)
Prompt orchestration with citations
RAG evaluation and quality dashboard

This works in demos… but fails in production.

Without the full system, you risk:

Models that degrade silently after deploy
Prompt chains that break on edge cases
Cost overruns from uncontrolled inference
Retrieval quality you can't measure or improve

What is Retrieval-Augmented Generation?

Retrieval-Augmented Generation (RAG) is an AI architecture that combines document retrieval with LLM generation to produce accurate, grounded responses. RAG systems embed documents into vectors, search for relevant context, and pass that context to an LLM for answer generation. Used by companies like Notion, Glean, and Microsoft Copilot to build AI products that leverage proprietary data.

Why this matters in production

RAG is the dominant pattern for building AI products over company data. At Notion, RAG powers AI search across millions of documents. Production RAG requires careful chunking strategies, hybrid retrieval, and evaluation pipelines — naive implementations produce hallucinations and miss critical context.

Common use cases

Building AI-powered search and Q&A over company documents and knowledge bases
Creating customer support chatbots grounded in product documentation
Implementing hybrid retrieval combining semantic search and keyword matching
Designing chunking pipelines that preserve document structure and context
Building RAG evaluation frameworks to measure retrieval and generation quality
Deploying production RAG with caching, monitoring, and cost optimization

RETRIEVA vs alternatives

RETRIEVA vs Fine-Tuning

RAG retrieves relevant context at query time from external data. Fine-tuning bakes knowledge into model weights. RAG is better for frequently changing data; fine-tuning for stable domain expertise. Most production systems use both.

RETRIEVA vs Prompt Engineering

RAG augments prompts with retrieved context automatically. Prompt engineering crafts prompts manually. RAG scales to large knowledge bases; prompt engineering works for smaller, static contexts.

RETRIEVA vs Knowledge Graphs

RAG uses vector similarity for retrieval. Knowledge graphs use structured relationships. Graph-RAG combines both approaches for better reasoning over complex, interconnected information.

Related skills

Vector Databases — RAG systems store embeddings in vector databases covered in
LLM Pipeline Engineering — RAG is a core pattern within LLM pipeline architecture in
LLM Evaluation — RAG quality is measured using evaluation frameworks from

Why this skill matters

RAG system design is the bridge into AI engineering. This skill proves you can build the retrieval layer that makes LLMs useful — the most in-demand AI capability in 2026.

Common questions about RETRIEVA

What is RAG in AI?

RAG (Retrieval-Augmented Generation) retrieves relevant documents and feeds them to an LLM as context. This grounds the model response in actual data, reducing hallucinations and enabling AI over proprietary information.

Is RAG still relevant in 2026?

RAG is more relevant than ever. As LLM context windows grow, RAG evolves but remains essential for cost-effective retrieval, citation grounding, and access control over sensitive documents.

How long does it take to build a RAG system?

A basic RAG prototype takes 1-2 days. Production RAG with proper chunking, hybrid retrieval, evaluation, and monitoring takes 4-8 weeks to build and tune.

RAG vs fine-tuning: which is better?

RAG is better for dynamic, frequently changing data. Fine-tuning is better for stable domain expertise and style. Most production systems combine both — fine-tuning for behavior, RAG for knowledge.

What are common RAG failures?

Poor chunking that loses context, naive embedding that misses relevant documents, lack of evaluation metrics, and no hybrid retrieval. Production RAG requires systematic optimization of each component.

Do data engineers need RAG skills?

RAG is the bridge skill between data engineering and AI engineering. Building retrieval pipelines, embedding infrastructure, and evaluation systems are data engineering problems with AI applications.