Production RAG playbook + LangChain 1.2.10 safeguards
Building production RAG got easier this week with a practical map of nine retrieval patterns and LangChain 1.2.10 fixes for token counting and context overflow. [9 RAG architectures](https://atalupadhyay.wordpress.com/2026/02/10/9-rag-architectures-every-ai-developer-must-know/)[^1] and a [prompt caching deep dive](https://atalupadhyay.wordpress.com/2026/02/10/prompt-caching-from-zero-to-production-ready-llm-optimization/)[^2] provide runnable labs and concrete optimization tactics. The [LangChain 1.2.10](https://github.com/langchain-ai/langchain/releases/tag/langchain%3D%3D1.2.10)[^3] and [langchain-core 1.2.10](https://github.com/langchain-ai/langchain/releases/tag/langchain-core%3D%3D1.2.10)[^4] releases add a token-counting fix and a new ContextOverflowError to harden pipelines. [^1]: Adds: Maps nine RAG patterns (Standard, Conversational, CRAG, Adaptive, Self-RAG, Fusion, HyDE, Agentic, GraphRAG) with diagrams and Python/LangChain labs (ChromaDB, optional Neo4j). [^2]: Adds: End-to-end prompt caching guide with provider-specific notes, labs (single/multi-turn, RAG), and production best practices. [^3]: Adds: Release notes including a fix for token counting on partial message sequences and internal provider rename. [^4]: Adds: Release notes adding ContextOverflowError (raised for OpenAI/Anthropic), improved approximate token counting, and minor docs/features.