Learning track 03
Retrieval & RAG
Understand vector search, enrichment, runtime branching, and production RAG implementation.
- Guides
- 4
- Sections
- 28
- Cards
- 115
Suggested sequence
Follow the track or enter where the problem begins.
Every guide is self-contained. The sequence only preserves prerequisite order when one topic depends on another.
- 01Vol. 09
RAG & Vector Search
Full route: why words cannot be compared directly → Bag of Words → TF-IDF → Word2Vec → contextual embeddings → cosine distance → how exactly the chunk “matches” with the query → HNSW → hybrid search → the full RAG pipeline parsed down to the byte.
- Sections
- 7
- Cards
- 24
- 02Vol. 13
Advanced RAG: Context & Enrichment
Deep analysis of the context enrichment layer in RAG: RAG vs Fine-tuning - when to choose what, advanced chunking strategies, query augmentation and decomposition, four patterns of context degradation, compression and hygiene, memory layers, incremental index updating, citations & grounding, prompt injection through documents.
- Sections
- 7
- Cards
- 24
- 03Vol. 18
RAG Runtime: From Cosine Similarity to the Final Answer
Not “what is RAG” in general terms, but what actually happens at the code and object level: what data structures are created during indexing, what exactly the vector store returns, who reacts to `score`, what if/else branches are triggered after retrieval, how rerank, packing, refusal, citations and debugging are done. This volume is about RAG as an executable program.
- Sections
- 7
- Cards
- 33
- 04Vol. 19
Production RAG in Python
A practical map of a minimal, but “adult” RAG service in Python. Not an abstract pipeline, but a repository structure, Pydantic models, FastAPI dependencies, retrieval service, vector store adapter, prompt/answer layer, reindex worker, observability and tests. The idea is simple: after this volume it should be clear what files the production RAG consists of and how the request moves through the code.
- Sections
- 7
- Cards
- 34