AI Engineering
A systematic curriculum for engineers transitioning into AI engineering, built on distributed systems intuition.
Query Transformations: Rewriting, HyDE, and Multi-Query
The query-side preprocessing layer for RAG: how rewriting, HyDE, multi-query, decomposition, and step-back prompting trade cost for recall.
Reranking: Cross-Encoders and Cascades
Why cross-encoders dominate the precision stage of retrieval, when a reranker pays off, and how to compose cascades that respect the latency budget.
Hybrid Search: BM25 Meets Dense Vectors
Why dense retrieval misses rare terms and exact matches, how BM25 and embeddings fuse via RRF, and the hybrid patterns that ship in production.
Chunking Strategies for Retrieval
Why chunk size is RAG's most undertuned variable, how recursive, semantic, and structural chunking differ, and when parent-document retrieval wins.
Vector Databases & ANN Indexes
How HNSW, IVF, and ScaNN trade recall for speed, why exact KNN doesn't scale, and how to pick between pgvector, Qdrant, and Pinecone in production.
Text Embeddings: Turning Meaning into Geometry
How embedding models encode text as dense vectors, why cosine similarity captures meaning, and how to build semantic search in Python and TypeScript.
LLM Inference: Tokens, Context, and Sampling
How LLMs process text: BPE tokenization, the context window as working memory, KV caching, and sampling parameters that shape output variance.