Overview

Build scalable, production-grade LLM pipelines using DSPy and the modern AI toolchain. LLM engineering is evolving beyond prompts and playgrounds. Today's applications demand modular, testable, and optimized workflows that can scale-while integrating multiple tools like LangChain, FAISS, Chroma, and FastAPI. Without the right foundation, it's easy to get stuck rewriting brittle code, debugging unpredictable outputs, or reinventing patterns that others have already solved. Optimizing Workflows Using the DSPy Framework is a practical, code-first guide to designing and deploying large language model workflows using DSPy as the backbone. You will learn how to structure systems that are debuggable, composable, and tuned for performance-whether you're building advanced RAG pipelines, connecting to vector stores, or wrapping your application in an API. What You Will Learn: - How to use DSPy to build modular, tunable LLM workflows - Techniques for chaining DSPy with LangChain, FastAPI, and vector databases - Strategies for implementing and optimizing RAG pipelines with DSPy - How to incorporate FAISS and Chroma for scalable retrieval - End-to-end pipeline examples in finance, healthcare, and manufacturing - Comparison of DSPy with LangChain, LangGraph, and agent-oriented models - How to debug, secure, and test DSPy workflows in production settings Includes fully working code examples to help you apply DSPy with real tools and real data-without guesswork. Grab your copy today and start building LLM systems that scale.

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