Building AI Applications with Haystack

Large language models are incredibly powerful, but connecting them to your own data sources is what makes them truly useful. Finding a structured, robust framework to orchestrate these connections is the key to building production-ready AI systems. This text-based course guides you from the fundamental concepts of LLM orchestration to constructing your own custom search and question-answering applications. You will learn how to connect document stores, design modular pipelines, and customize components to build reliable AI-driven workflows. What you'll learn: • Understand the core architecture of Haystack, including components, builders, and pipelines. • Configure document stores and integrate vector databases to index your custom text data. • Build robust Retrieval-Augmented Generation (RAG) pipelines that ground LLM responses in real facts. • Apply prompt engineering patterns directly within your application workflows. • Design custom components to extend the framework's capabilities for unique use cases. • Practice debugging and evaluating pipeline performance to ensure accurate and safe outputs. You will start with the essential terminology and architectural foundations before moving on to hands-on pipeline construction and advanced integration patterns. The written explanations and clear code examples will help you transition smoothly from theory to practical implementation. This course is designed for software developers, data practitioners, and tech enthusiasts who want to build AI applications, requiring no prior experience with Haystack. Start reading today to build smarter, data-connected AI applications.