Prompt Flow Basics: Build AI Systems to Chat with Documents

Connecting large language models to your own documents enables accurate, context-aware answers without manual data searching. This written course guides you through creating robust conversational workflows using Prompt Flow, bridging raw documents with modern generative AI techniques. You will begin with essential terminology, including prompt engineering foundations, embeddings, and retrieval-augmented generation (RAG) principles. Step by step, you will read through clear technical guides and code walkthroughs to assemble functional pipelines that parse text files, retrieve relevant context, and deliver precise responses. What you'll learn: - Understand core concepts of prompt orchestration, embeddings, and retrieval-augmented generation - Structure document ingestion and vector search nodes inside Prompt Flow - Write custom Python tools to preprocess data and format language model outputs - Configure multi-turn conversational memory for chat applications - Evaluate output quality and pipeline accuracy using structured metrics - Apply debugging and prompt refinement techniques to handle edge cases The course progresses logically from core definitions to complete workflow construction and performance evaluation. Designed for beginners and developers exploring practical AI development, no prior experience with Prompt Flow is required—only basic familiarity with Python and general programming concepts. Start reading today to build dependable document-driven AI workflows with confidence.