Turn an LLM's unpredictable text into typed, trustworthy data with Pydantic v2: schemas with good descriptions, validators, native structured outputs, and validation-feedback retries.
Share your comments or suggestions to improve our content.
Learn the basics of LangChain to build AI applications. From installation to your first functional pattern.
Learn to treat your agent's context window as the finite resource it is: the four levers of context engineering (write, select, compress, isolate), compaction with structured summaries, persistent memory outside the context, just-in-time retrieval, subagents with isolated windows, and tool-result hygiene. With production-ready Python code and the mistakes that silently degrade your agent. New expansion: the context-failure taxonomy (poisoning, distraction, confusion, clash), reasoning budgets with interleaved extended thinking, the real cost of multimodal context, managed memory (Letta, Zep, mem0), effective context length per RULER, and multi-agent handoffs with structured payloads. Latest expansion: the positional anatomy of context (lost in the middle and cache-aware placement), isolating untrusted content by design with Dual-LLM and CaMeL, and generating outputs longer than the window with outlines, rolling summaries, and patch-based revision. August extension: per-section token budgets with a degradation ladder, prompt compression with LLMLingua, semantic retrieval deduplication, self-hosted KV cache (vLLM and SGLang) with cache-aware routing, and memory evaluation with LongMemEval.
Battle-tested patterns for building production-ready RAG (Retrieval-Augmented Generation) systems in 2026: semantic chunking, embedding selection, hybrid search, and cross-encoder reranking.