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AI Engineering · 200-level

RAG Foundations

Sit Chuun — free All modules
AIE-2023 creditscorebadge: rag-iprereqs: AIE-103
Start this course — free

Earn the rag-i credential

What's inside

Sections & lessons

01

Why retrieval

  • Grounding, and when RAG beats fine-tuningconcept45 min
02

Chunking strategies

  • Size/overlap tradeoffs and the cost consequenceconcept45 min
03

Chunk Slicer lab

  • Watching search quality rise then crash as one slider movesconcept90 min
04

Embeddings & vector similarity

  • How similarity search works, made literally draggableconcept45 min
05

Vector stores in practice

  • pgvector in practice: indexing and nearest-neighbor lookupconcept60 min
06

Retrieval: top-k & filters

  • Ranking chunks and filtering the candidate poolconcept45 min
07

The lost-answer failure mode

  • Watching a correct chunk never make the visible top-kconcept60 min
08

Grounded generation

  • Citations and refusal on low confidenceconcept60 min
09

Lab + eval-gate: build a RAG bot

  • RAG Sandbox graded lab — retrieval score ≥ threshold (RAG I badge)concept120 min
Start this course — free
Learn it from the inside

This module's playgrounds

  • RAG Sandbox

    Coming soon

    See why that chunk got retrieved, in 2D space.

    AiAIE-202BYOK AIDeep
    Play it now

  • Meaning Map

    Coming soon

    King minus man plus woman, mapped and clickable.

    AiAIE-202BYOK AIDeep
    Play it now

  • Retrieval Race

    Coming soon

    Three search strategies race for the same buried answer.

    AiAIE-202Browser (JS)Medium
    Play it now

  • Chunk Slicer

    Coming soon

    Drag one slider, watch search quality rise then crash.

    AiAIE-202Browser (JS)Medium
    Play it now

  • Truth Meter

    Coming soon

    Same confident tone, one answer is grounded truth.

    AiAIE-202BYOK AIDeep
    Play it now
AI Engineering

Next in this path

Where this specialization goes from here — in (level, code) order.

  • AIE-203

    RAG Quality & Retrieval Evals

    2 creditselectivebadge: rag-ii
    Start
  • AIE-204

    Structured Outputs & Function Calling

    2 creditselectivebadge: structurer
    Start
  • AIE-205

    Automation & Workflows

    2 creditselectivebadge: automator
    Start
  • AIE-301

    Agent Foundations

    3 creditscorebadge: agent-i
    Start
  • AIE-302

    Multi-Agent Orchestration & HITL

    2 creditselectivebadge: agent-ii
    Start
  • AIE-303

    MCP & Tool Integration

    2 creditselectivebadge: integrator
    Start
  • AIE-304

    AI Security & Red-Teaming

    3 creditselectivebadge: red-teamer
    Start
  • AIE-401

    Evals & Quality Systems

    3 creditscorebadge: evaluator
    Start
Vocabulary

Key concepts in this course

Retrieval-Augmented Generation (RAG) Grounding Chunking Embedding Cosine Similarity Vector Database HNSW Index Reranking Recall@k

Compare related approaches

RAG vs Fine-Tuning Vector Search vs Keyword Search Small Chunks vs Large Chunks
Optional · watch & try

Go deeper, elsewhere

Hand-picked public explainers and open tools — always optional, never required, never graded.

Introduction to Vector Embeddings and Vector SearchFoundational explanation of turning text into vectors for similarity search Embeddings & Vector Databases ExplainedConnects embeddings to how a vector database actually stores and searches them Vector Databases simply explained! (Embeddings & Indexes)Covers embedding indexes (e.g. HNSW) at a beginner level Embedding Projector (TensorBoard, Hugging Face Space)Interactive 3D projection of embedding vectors to see semantic clusters and analogies Retrieval Augmented Generation (RAG) Explained in 8 Minutes!Fast end-to-end mental model of the RAG pipeline · ~8 minRAG Explained in 12 MinutesSecond angle on the same pipeline with a different worked example · ~12 min

This module ends in a gate you can fail.

That's what makes passing it mean something. Sit Chuun, get placed, and start earning.

Sit Chuun — free All modules