Learn the basics of LangChain to build AI applications. From installation to your first functional pattern.
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Battle-tested patterns for building production-ready RAG (Retrieval-Augmented Generation) systems in 2026: semantic chunking, embedding selection, hybrid search, and cross-encoder reranking.
Learn how to build evals for LLM applications: datasets from real traffic, deterministic checks, LLM-as-judge with binary rubrics, CI integration, and the biases and common mistakes that silently invalidate your metrics. Includes a complete pytest harness ready to use.
Learn how prompt caching works in the Anthropic, OpenAI, and Gemini APIs: what the cacheable prefix is, how to order your prompt to maximize hits, breakpoints with cache_control, TTLs and pricing, and the mistakes that silently invalidate the cache. With production-ready Python code and metrics to measure your hit rate. Extended edition: the KV cache under the hood, Anthropic automatic caching and the 1-hour TTL, OpenAI prompt_cache_key and 24h retention, Gemini implicit and explicit caching, the math of the savings, multi-tenant and cache-aware routing, RAG, agents and compaction, self-hosted with vLLM and SGLang, security, observability and alerts, a CI smoke test, a migration guide, anti-patterns, a full case study, and an FAQ.