O’Reilly Technical Guide: RAG in Production with Haystack
Building Trustworthy, Scalable, Reliable, and Secure AI Systems - Access this O’Reilly resource for a step-by-step guide on how to build enterprise-grade AI Retrieval-Augmented Generation (RAG) applications with Haystack, deepset’s easy to use framework for modular LLM orchestration.
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 by deepset comes in. Our open source AI Orchestration framework provides a collection of the most useful tools, integrations, and infrastructure building blocks to help you design and build scalable, API-driven LLM backends for generative AI use cases.
With Haystack, it's easy to build Compound AI systems such as retrieval augmented generation (RAG), agentic RAG and agents, extractive and generative QA, or custom enterprise search systems. Each chapter of this guide provides considerations for building production-ready RAG systems and offers ML engineers, AI engineers, and backend engineers a practical blueprint for the LLM software development lifecycle.
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