RAG 101 for AI Engineers: From Naive Retrieval to Production-Grade PipelinesChunking, embeddings, reranking, citations, evaluation, and failure modes explained simply.
A step-by-step guide to building a reliable RAG system, covering chunking, embeddings, retrieval, reranking, context windows, and evaluation tactics for better answers.
Quickstart: Build a Memory-Enabled AI Assistant with RAG in a WeekendA minimal architecture that scales: ingestion, retrieval, conversation state, and observability.
Follow a practical quickstart to create a memory-enabled AI assistant using RAG, including ingestion, indexing, conversation state, caching, and basic monitoring.
Beyond Brute Force: A Practical Guide to Vector Search Algorithms for AI EngineersFrom exact k-NN and HNSW to IVF-PQ, hybrid lexical-vector retrieval, learned indexes, and binary embeddings - choosing the right retrieval strategy for production AI systems
A practical engineering guide to vector search: exact k-NN, HNSW, IVF, product quantization, hybrid lexical+vector search, learned indexes, and binary embeddings compared
High-Performance AI Search: Building Fast, Accurate Retrieval PipelinesHow to architect vector search systems that are both fast and accurate, using hybrid retrieval, reciprocal rank fusion, and cross-encoder reranking
A practical guide to high-performance AI search: multi-stage retrieval architecture, reciprocal rank fusion, cross-encoder reranking, and latency budgeting for production systems.