Course Outline

Advanced RAG Engineering Crash Course

Build retrieval-augmented generation systems that remain useful under real-world constraints: imperfect documents, ambiguous queries, changing corpora, strict access rules, latency budgets, and measurable quality requirements.

Duration
4 Weeks
Level
Intermediate to Advanced
Outcome
Ship a production-minded RAG system

What You Will Learn

This is a four-week course because advanced RAG is a complete retrieval-and-generation system, not merely a vector-database integration. The course prioritizes the highest-leverage skills: retrieval design, context quality, agentic and multimodal extensions, evaluation, safety, and production operations.

Crash Course Outcomes

  • Build and benchmark sparse, dense, and hybrid retrieval baselines
  • Choose chunking, embedding, metadata, filtering, and indexing strategies for a target corpus
  • Improve context quality with query transformation, fusion, reranking, compression, and citations
  • Decide when Graph RAG, agentic RAG, or multimodal RAG is warranted—and when it adds needless complexity
  • Evaluate retrieval and generation separately, diagnose failures, and reduce unsupported claims
  • Optimize an end-to-end RAG workflow for latency, throughput, cost, freshness, and production observability
  • Operate a RAG system with access controls, guardrails, regression tests, release gates, and incident-ready traces

Recommended Audience and Prerequisites

  • Audience: ML engineers, search engineers, backend engineers, data scientists, and AI product builders implementing knowledge-grounded applications
  • Prerequisites: working knowledge of embeddings, LLM prompts, APIs, and basic Python
  • Helpful background: RAG fundamentals, Text embeddings, and Prompt development
  • Difficulty: intermediate to advanced; begin with the basic retrieval baseline even if the target architecture is agentic or graph-based

Week 1: Engineer a Retrieval Baseline That You Can Trust

Intermediate

Goal: Establish a simple, measurable retrieval baseline before adding advanced retrieval or generation techniques.

Learning objectives:

  • Map corpus sources, permissions, freshness needs, and document lifecycle risks
  • Compare lexical and dense retrieval using the same labeled test set
  • Select chunking and metadata strategies based on document structure and retrieval evidence

Syllabus:

  • RAG system anatomy: ingestion, parsing, chunking, metadata, embeddings, indexing, retrieval, context assembly, generation, and observability
  • Basic retrieval systems: lexical search, dense vector search, exact-match behavior, semantic similarity, and the limits of each approach
  • Chunking strategies: fixed-size, recursive, semantic, document-aware, parent-child, and late chunking; overlap, chunk identity, metadata, and retrieval granularity

Week 2: Improve Context Quality with Hybrid, Reranked, and Graph Retrieval

Intermediate to Advanced

Goal: Combine retrieval signals and relationship-aware evidence without treating every advanced technique as a default.

Learning objectives:

  • Build and tune hybrid retrieval with sparse and dense signals
  • Use reranking, query rewriting, and context compression to improve evidence quality
  • Determine whether Graph RAG solves a genuine relationship or multi-hop problem

Syllabus:

  • Reranking and context selection: cross-encoders, late interaction, top-$k$ and top-$n$ tuning, duplicate removal, diversity, context compression, and source attribution
  • Graph RAG: entity extraction, graph schema, local and global retrieval, graph traversal, community summaries, provenance, and the operational cost of graph maintenance

Week 3: Build Agentic and Multimodal RAG Deliberately

Advanced

Goal: Add dynamic retrieval and non-text evidence only where they improve outcomes enough to justify the extra reliability, latency, and security work.

Learning objectives:

  • Design bounded agentic retrieval loops with explicit tools, evidence checks, and termination conditions
  • Retrieve and reason over images, tables, diagrams, audio, or video alongside text
  • Secure untrusted retrieved content and prevent retrieval from becoming an authorization bypass

Syllabus:

Week 4: Evaluate, Optimize, and Operate RAG in Production

Advanced

Goal: Turn the RAG system into an observable service with defensible metrics, latency budgets, release gates, and production feedback loops.

Learning objectives:

  • Measure retrieval, context, answer, safety, and operations metrics as a connected system
  • Identify and reduce the largest sources of RAG latency and cost
  • Monitor corpus, index, retrieval, and answer-quality regressions after deployment

Syllabus:

  • Evaluation metrics and datasets: Recall@$k$, Precision@$k$, MRR, NDCG, context precision and recall, groundedness, citation correctness, answer relevance, abstention, safety, latency, and cost
  • Debugging and error taxonomy: ingestion, chunking, embedding, retrieval, reranking, context construction, generation, citations, access filtering, and system availability
  • Latency and cost optimization: parallel retrieval, caching, precomputation, approximate nearest-neighbor search, dynamic $k$, context budgets, smaller rerankers, model routing, and graceful degradation
  • Production monitoring: traces, source and index versions, retrieval distributions, no-result rate, citation coverage, quality feedback, freshness lag, permission denials, alerts, canaries, and rollback

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