RAG and tool-enabled AI applications are built to do more than generate free-form responses. In this hands-on course, you’ll learn RAG and Tool Calling with DeepSeek, a practical course that helps developers create structured, reliable, tool-enabled, and knowledge-grounded AI applications. Whether you want to validate AI responses, connect models with external tools, retrieve information from documents, or reduce hallucinations, this course gives you a structured starting point.

You’ll begin by exploring structured output and reliable response design, including JSON structures, schemas, reasoning modes, Pydantic models, field constraints, validation errors, response repair, and fallback strategies. Then, you’ll move into function calling and external tool integration by working with JSON Schema, calculator tools, external APIs, authentication, timeouts, restricted SQLite access, multi-tool routing, and failure recovery. Finally, you’ll explore Retrieval-Augmented Generation with document loading, text chunking, embeddings, vector stores, semantic retrieval, top-K selection, metadata filtering, grounded generation, source attribution, hallucination control, and RAG application testing. By the end of this course, you will be able to: -Design structured DeepSeek responses using JSON schemas, Pydantic models, field types, constraints, and validation rules. -Implement response validation, repair, and fallback strategies for reliable AI application workflows. -Integrate DeepSeek with external tools, APIs, and restricted databases using function calling, JSON -Schema, and safe execution practices. -Develop RAG pipelines using document processing, embeddings, vector stores, semantic retrieval, top-K selection, and metadata filtering. -Evaluate grounded DeepSeek responses for source relevance, hallucination control, unsupported questions, and overall application reliability. This course is designed for Python developers, AI application developers, backend engineers, software engineers, and anyone who wants to build reliable applications using DeepSeek. If you are familiar with basic DeepSeek API usage and want a practical path from structured responses to function calling and Retrieval-Augmented Generation, this course provides a guided learning experience. You should have basic experience with Python, JSON, APIs, and command-line usage. Familiarity with DeepSeek or other LLM APIs, Python virtual environments, and basic database concepts is helpful, along with a willingness to practice through hands-on AI application development tasks. Enroll now and learn how to build reliable, tool-enabled, and knowledge-grounded AI applications with DeepSeek.