Step-by-step tutorials on building voice and chat AI agents using OpenAI Agents SDK, Realtime API, function calling, multi-agent orchestration, and production deployment patterns.
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Understand what Retrieval-Augmented Generation is, why it exists, how the core architecture works, and when to choose RAG over fine-tuning for grounding LLM responses in your own data.
Learn the most effective document chunking methods for RAG pipelines including fixed-size, semantic, and recursive splitting, with guidance on overlap, chunk sizes, and markdown-aware strategies.
A complete hands-on tutorial that walks you through building a working RAG pipeline from scratch — loading documents, chunking, embedding, storing in a vector database, retrieving, and generating answers.
Compare embedding models for RAG pipelines across dimensions, retrieval quality, latency, and cost — including OpenAI text-embedding-3, Cohere embed-v3, and open-source sentence-transformers alternatives.
Learn how to implement hybrid search for RAG by combining BM25 keyword search with vector similarity, using reciprocal rank fusion and re-ranking to maximize retrieval quality.
Learn how to evaluate RAG pipelines systematically using the RAGAS framework, measuring faithfulness, answer relevancy, context recall, and context precision to identify and fix retrieval failures.
Go beyond basic RAG with advanced retrieval patterns including query rewriting, hypothetical document embeddings (HyDE), step-back prompting, and iterative multi-step retrieval chains.
Learn how to use metadata filtering in RAG to narrow vector search results using structured attributes like document type, date ranges, departments, and access levels for more precise retrieval.
