Building RAG Agents with LLMs
The evolution and adoption of large language models (LLMs) have been nothing short of revolutionary, with retrieval-based systems at the forefront of this technological leap.
The evolution and adoption of large language models (LLMs) have been nothing short of revolutionary, with retrieval-based systems at the forefront of this technological leap. These models are not just tools for automation; they are partners in enhancing productivity, capable of holding informed conversations by interacting with a vast array of tools and documents. This course is designed for those eager to explore the potential of these systems, focusing on practical deployment and the efficient implementation required to manage the considerable demands of both users and deep learning models. As we delve into the intricacies of LLMs, participants will gain insights into advanced orchestration techniques that include internal reasoning, dialog management, and effective tooling strategies.
Learning Objectives
- Compose an LLM system that can interact predictably with a user by leveraging internal and external reasoning components.
- Design a dialog management and document reasoning system that maintains state and coerces information into structured formats.
- Leverage embedding models for efficient similarity queries for content retrieval and dialog guardrailing.
- Implement, modularize, and evaluate a RAG agent that can answer questions about the research papers in its dataset without any fine-tuning
