Prompt & Context Engineering
Retrieval Augmented Generation
Give the model the documents instead of hoping it memorised them.
You can jump straight in, but this star assumes Context Engineering. Not recommended as a first stop.
Four layers of depth
Each layer ends with a quiz. Finish layer 4 and you own this concept.
- L1IntuitionWhy a model that has read the whole internet still can't answer questions about your company, and how retrieval fixes it.6m
40 XP - L2MechanicsThe pipeline in detail: chunking, embeddings, vector search, hybrid retrieval, reranking, and prompt assembly.9m
70 XP - L3CodeBuild the whole loop: chunk with overlap, embed, store in pgvector, hybrid search with RRF, and assemble a cited prompt.12m
110 XP - L4FoundationsThe geometry of similarity, why ANN search is approximate, HNSW's structure, and the recall-latency-memory triangle.12m
180 XP
33 stars in the atlas.