Retrieval-Augmented Generation
Builds and measures a grounded retrieval system end to end.
Tech stack
Modules
6 modules · 56 hours
M1 · RAG architecture
8 h- Why retrieval
- Indexing versus query pipeline
- Grounding and citation
- Failure modes
- When RAG is the wrong tool
Lab: Baseline RAG implementation end to end
Course material for this module is in production.
M2 · Ingestion and parsing
10 h- Document loaders
- PDF/HTML/DOCX and table extraction
- OCR
- Layout preservation
- Metadata extraction
- Incremental sync
- Deduplication
- Refresh scheduling
Lab: Ingest a heterogeneous corpus including scanned and tabular documents
Course material for this module is in production.
M3 · Chunking strategy
10 h- Fixed, recursive, semantic, parent-document and hierarchical chunking
- Overlap
- Chunk size versus recall
- Metadata design for filtering
Lab: Controlled chunking experiment measured on retrieval metrics
Course material for this module is in production.
M4 · Embeddings and vector stores
12 h- Embedding model selection and benchmarking
- Dimensionality
- Normalisation
- Index types: flat, HNSW, IVF
- Distance metrics
- Filtering and hybrid metadata queries
- Upserts
- Sharding and scale
Lab: Build and tune a vector index for recall and latency
Course material for this module is in production.
M5 · Retrieval quality
10 h- Dense versus sparse (BM25)
- Hybrid fusion
- Reranking with cross-encoders
- Query rewriting and expansion
- Multi-hop retrieval
- Top-k selection
- Context assembly and ordering
Lab: Raise recall@k and precision with hybrid search plus reranking
Course material for this module is in production.
M6 · Generation layer
6 h- Grounded prompting
- Citation enforcement
- Context-window packing
- Conflicting-source handling
- Answerability and abstention
Lab: Citation-enforced answering with abstention on insufficient evidence
Course material for this module is in production.