Large Language Models (LLMs) have transformed the way we access and generate information, but they remain limited by the static nature of their training data. Retrieval-Augmented Generation systems offer a powerful solution by combining language models with search capabilities. These systems dynamically retrieve relevant content from external sources, allowing intelligent agents to provide up-to-date and context-aware responses. As a result, search engines and retrieval modules have become integral to modern AI systems, enabling more accurate and grounded decision-making.

This course begins by introducing foundational concepts in information retrieval. You will explore the structure of documents, queries, and collections, and learn how to evaluate relevance in large-scale systems. Key topics include document indexing, Boolean and ranked retrieval models, query expansion techniques, and evaluation using standard benchmarks. These methods form the basis of effective search infrastructure and are critical to supporting dynamic reasoning in downstream applications.

In the second half of the course, you will design and implement a lightweight multi-agent system that leverages LLMs to interact with structured and unstructured data sources. This system will demonstrate how LLM agents coordinate, share knowledge, and make decisions in real time. You will develop skills in LLM communication protocols, simple agent planning, and data-driven reasoning, preparing you to build intelligent systems capable of integrating retrieval into complex workflows. You will also explore retrieval-augmented generation (RAG), prompt engineering, agent memory, and tool use with LLM APIs.