AdvancedBuilding RAG properly: chunking, evaluation, reranking
Most RAG fails at retrieval, not generation. Build a golden set, measure recall@k, run chunking experiments, add a reranker when data says so.
RAG
Retrieval-augmented generation done properly — chunking, retrieval evaluation, reranking, and the vector index underneath.
Advanced
AdvancedVector databases under the hood: HNSW and IVF-PQ
ANN search trades accuracy for speed. How HNSW graphs and IVF-PQ quantisation work, which knobs move recall, and how to choose an index.
IntermediateBuild a chatbot that answers from your own PDFs in Python
Retrieval-augmented generation in plain Python: split a PDF into chunks, find the relevant ones, and let Claude answer using only those. No vector database required.
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