Beginner

LLM Integration — Beginner

Build a production RAG pipeline from anatomy through deployment, including prompting, chains, vector store integration, and a Q&A bot capstone.

5 lessons ~225 min total
  1. 1

    Anatomy of a RAG Pipeline

    Understand the five stages every RAG system shares — from ingest to generation — and the mental model that turns 'advanced RAG' techniques from mysterious to obvious.

    45 minChunkingRetrievalPrompt Assembly
  2. 2

    Prompts & Templates

    The prompting layer that turns retrieved chunks into useful LLM responses. Messages array semantics, templates for maintainability, few-shot examples, structured outputs that don't break downstream parsers, and the production hygiene that keeps prompts good.

    45 minPrompt TemplatesFew-ShotStructured Outputs
  3. 3

    Retrieval Chains

    Beyond single-pass RAG. Query rewriting closes the question-statement gap. HyDE searches with hypothetical answer vectors instead of question vectors. Multi-step retrieval loops and decomposes. Plus the chain composition patterns that hold a production pipeline together.

    45 minQuery RewritingHyDEChain Composition
  4. 4

    Vector Store Integration

    Wire a real vector store into your RAG pipeline. The store landscape and selection criteria, client lifecycle and connection management, metadata filtering as a quality lever, batched upserts with stable IDs, and when to choose framework abstractions versus direct vendor clients.

    45 minClient LifecycleBatched UpsertsFramework vs Direct
  5. 5

    Building a Q&A Bot (Capstone)

    The synthesis of Days 1-4 into a complete production Q&A bot. Ingestion pipeline, full query chain, citation-aware answers with refusal, and the gold-set evaluation + shadow/A/B rollout discipline that turns a RAG demo into a RAG product.

    45 minCapstoneCitationsShadow Rollout