Building Agentic AI Systems with DSPy: From Reasoning Modules to Tool-Using LLMs Are you frustrated by fragile AI prototypes that can't handle real-world complexity? Many developers struggle to weave large language models (LLMs) into reliable, tool-using agents. "Building Agentic AI Systems with DSPy" shows you how to transform those prototypes into robust, production-ready pipelines. This book presents DSPy's signature-based programming model, which bridges LLM reasoning and external tools-databases, vector stores, APIs ...
Read More
Building Agentic AI Systems with DSPy: From Reasoning Modules to Tool-Using LLMs Are you frustrated by fragile AI prototypes that can't handle real-world complexity? Many developers struggle to weave large language models (LLMs) into reliable, tool-using agents. "Building Agentic AI Systems with DSPy" shows you how to transform those prototypes into robust, production-ready pipelines. This book presents DSPy's signature-based programming model, which bridges LLM reasoning and external tools-databases, vector stores, APIs-while enforcing type safety and runtime checks. You'll learn to configure LLM backends, integrate MLflow for end-to-end tracing, and craft modules that validate inputs and guard against hallucinations. From constructing retrieval-augmented generation (RAG) workflows to implementing ReAct agents that alternate between "thought" and "action" steps, this guide equips you to build agents that actually solve problems. You'll gain hands-on skills and insights, including: Mastering DSPy's @signature decorator to enforce input/output schemas and catch errors early Building custom tools-wrappers for REST APIs, database queries, and third-party SDKs-that slot seamlessly into agent loops Designing RAG pipelines: connecting vector stores, retrieving relevant context, and feeding it into prompt templates for accurate responses Implementing ReAct patterns: orchestrating LLM reasoning alongside actions, handling retries, and incorporating Assert and Suggest for self-correction Automating prompt and few-shot example tuning with DSPy's Optimizer to maximize accuracy and minimize token costs Packaging agents into Docker containers, deploying to cloud platforms (AWS, GCP, Azure), and setting up CI/CD pipelines for continuous delivery Monitoring production systems: setting up MLflow tracking servers, capturing metrics, visualizing execution graphs, and debugging step by step Establishing self-improving loops that harvest user feedback, re-optimize pipelines in production, and ensure your agent evolves with changing data Securing workflows: fetching secrets from vaults, enforcing parameterized queries, and validating user inputs to prevent injections and data leaks Whether you're a developer, data scientist, or AI engineer, this book arms you with practical, battle-tested patterns for creating agentic AI systems that scale. Ready to build reliable, tool-driven agents that deliver real value? Grab your copy of "Building Agentic AI Systems with DSPy" today and start engineering the next generation of AI solutions.
Read Less
Add this copy of Building Agentic AI Systems with DSPy: From Reasoning to cart. $14.49, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
Add this copy of Building Agentic Ai Systems With Dspy: From Reasoning to cart. $18.11, new condition, Sold by Just one more Chapter rated 3.0 out of 5 stars, ships from Miramar, FL, UNITED STATES, published 2025 by Independently published.
