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Building Retrieval-Augmented Agents with DSPy: Declarative RAG Systems with Python and LLMs (Design, Optimize, and Deploy Modular RAG Agents with DSPy by Roberto Pizzlo

Building Retrieval-Augmented Agents with DSPy: Declarative RAG Systems with Python and LLMs (Design, Optimize, and Deploy Modular RAG Agents with DSPy

by Roberto Pizzlo
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Book cover type: Paperback
  • ISBN13: 9798288451201
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 130
  • Original Price: GBP 11.06
  • Language: English
  • Edition: N/A
  • Item Weight: 241 grams
  • BISAC Subject(s): Artificial Intelligence / Expert Systems

Unlock the full potential of Retrieval-Augmented Generation (RAG) with Building Retrieval-Augmented Agents with DSPy, the definitive guide to designing modular, declarative, and self-improving AI systems using Python and large language models (LLMs).

Whether you're an AI developer, machine learning engineer, backend architect, or NLP researcher, this book delivers a hands-on, end-to-end approach to constructing intelligent agents using the powerful DSPy framework. You'll learn how to build robust RAG pipelines that seamlessly integrate vector search, document chunking, tool calling, memory handling, and modular LLM orchestration-while maintaining full control over optimization and traceability.

Written by Roberto Pizzlo, a leading voice in the field of modern AI systems, this book is your launchpad into scalable, production-ready agentic workflows. With concise explanations, up-to-date DSPy examples, and best practices drawn from real-world deployments, this guide equips you to build agents that reason, retrieve, revise, and respond with precision.

Inside You'll Learn:

  • How to construct declarative RAG pipelines using DSPy's signature-driven programming

  • Techniques for chunking, embedding, indexing, and hybrid retrieval using FAISS, Qdrant, and Weaviate

  • How to integrate OpenAI, Claude, Cohere, or local LLMs via DSPy modules

  • Optimizing agents using MIPROv2, BootstrapFewShot, and assertion-based correction loops

  • Tool-augmented workflows combining search, calculators, summarizers, and code interpreters

  • Real-world case studies on building enterprise-grade knowledge bots

  • Deployment strategies with FastAPI, Docker, and local embedding models via Ollama

  • Future directions in modular RAG, memory-augmented agents, and self-adaptive pipelines

Why This Book?

Unlike abstract theory-heavy texts, this guide focuses on practical implementation, real-world design patterns, and scalable agent infrastructure. It's built for the realities of 2024 and beyond, where precision, observability, and modular design are essential for AI production systems.

If you're working with DSPy, Python, RAG, LLMs, retrievers, vector search, or frameworks like LangChain, LlamaIndex, or AutoGen, this book is a must-have resource in your AI development toolkit.

About the Author:

Roberto Pizzlo is a seasoned AI systems engineer and author with a growing portfolio of influential titles on intelligent agents, modular LLM architecture, and generative AI frameworks. Known for his clarity, technical depth, and practical insight, Pizzlo has helped thousands of professionals master cutting-edge AI technologies and deploy scalable NLP pipelines across industries.

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LLM engineers, AI developers, data scientists, backend teams, NLP practitioners, MLops professionals, and anyone building next-gen AI applications.

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