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Introduction: LLM outputs are inherently unstructured—models generate text, not data structures. Yet most applications need structured data: JSON for APIs, typed objects for business logic, specific formats for downstream processing. Output parsing bridges this gap, transforming free-form text into reliable, validated data structures. This guide covers the techniques that make parsing robust: format specification in […]

Introduction: Basic RAG retrieves documents and stuffs them into context. Advanced RAG transforms retrieval into a sophisticated pipeline that dramatically improves answer quality. This guide covers the techniques that separate production RAG systems from prototypes: query rewriting to improve retrieval, hybrid search combining dense and sparse methods, cross-encoder reranking for precision, contextual compression to fit […]

Introduction: Deploying LLMs to production is fundamentally different from deploying traditional ML models. The models are massive, inference is computationally expensive, and latency requirements are stringent. This guide covers the strategies that make LLM deployment practical: model optimization techniques like quantization and pruning, inference serving with batching and caching, containerization with GPU support, auto-scaling based […]