This course focuses on overcoming common challenges in Retrieval-Augmented Generation (RAG) systems using agentic approaches and modern LLM techniques. It is designed for AI practitioners, engineers, and developers who want to build more reliable, scalable, and efficient RAG pipelines.

The course begins by analyzing the limitations of classic RAG, identifying key issues that affect retrieval quality, accuracy, and context handling. Learners then explore agentic approaches, which integrate autonomous decision-making and multi-step reasoning to improve retrieval and response generation.

A practical guide is provided for choosing between RAG, in-context learning, and fine-tuning for specific projects, including case studies that compare the effectiveness of each method. The course also covers integrating RAG with knowledge graphs to provide structured context, as well as the critical role of vector databases in enhancing retrieval performance.

Additionally, learners examine how prompt engineering complements RAG and fine-tuning, learning strategies for optimizing LLM responses while maintaining scalability. By the end of the course, participants will be able to design hybrid, agentic RAG pipelines that solve complex retrieval and generation challenges in real-world AI applications.