Retrieval-Augmented Generation in Production with Haystack

Building Trustworthy, Scalable, Reliable, and Secure AI Systems

Book Details

Book Description

In today's rapidly changing AI technology environment, software engineers often struggle to build real-world applications with large language models (LLM). The benefits of incorporating open source LLMs into existing workflows is often offset by the need to create custom components. That's where Haystack comes in. This open source framework is a collection of the most useful tools, integrations, and infrastructure building blocks to help you design and build scalable, API-driven LLM backends.

With Haystack, it's easy to build extractive or generative QA, Google-like semantic search to query large-scale textual data, or a reliable and secure ChatGPT-like experience on top of technical documentation. This guide serves as a collection of useful retrieval-augmented generation (RAG) mental models and offers ML engineers, AI engineers, and backend engineers a practical blueprint for the LLM software development lifecycle.

This book is published as open-access, which means it is freely available to read, download, and share without restrictions.

If you enjoyed the book and would like to support the author, you can purchase a printed copy (hardcover or paperback) from official retailers.

Download and Read Links

Share this Book