Course Outline
This course teaches developers how to integrate Retrieval-Augmented Generation (RAG) into their applications to enhance the performance of large language models (LLMs). You'll start with the fundamentals of RAG, explore real-world use cases, and build practical skills using Cohere's SDK and open-source tools. The course walks you through embedding documents, retrieving relevant context, and crafting chat prompts that reduce hallucinations and boost accuracy. By the end, you'll create your own RAG-powered app that responds to user queries with grounded, context-aware answers.
Learning Outcomes
- Learn how to embed and retrieve documents to power RAG workflows
- Implement a working RAG pipeline using Cohere and Python
- Build smarter apps that answer user questions with real, relevant data
Who Is This Course For?
This course is designed for developers, technical product managers, and AI enthusiasts who want to go beyond generic LLM outputs and add contextual intelligence to their applications. Whether you're building a chatbot, knowledge assistant, or internal tool, this course gives you the skills to harness external data and make your AI more reliable.
Why Enroll?
Most LLMs can sound convincing—but not always accurate. With RAG, you can ground your model’s responses in real, trustworthy data, reduce hallucinations, and give your app a competitive edge. By enrolling in this course, you’ll move from theory to practice, learn from real code examples, and build something that goes far beyond simple prompt engineering.
Pre-requisites
- Familiarity with Python and basic programming concepts
- Understanding of APIs and JSON data formats
- Prior exposure to LLMs or chatbot frameworks (helpful but not required)
Let’s Get Started!
Don’t just chat—build.
Learn how to bring RAG into your applications with real code, real APIs, and real power.
