In this course, we’ll learn how to optimize files and data for Retrieval-Augmented Generation, or RAG.
By the end of this course, you’ll have actionable steps you can follow to improve the quality of the responses an LLM generates when using a custom knowledge source.
RAG combines two powerful concepts: retrieval and generation. It allows your AI agent to pull precise information from vast data sources, like a product catalog or list of policies, and then use language models to generate natural, informative responses. This means an agent that not only gives an answer but provides the right answer from a trusted source—quickly and accurately.
But here’s the thing: the quality of your agent’s responses relies heavily on the quality and structure of the data you feed it. If the data going in is cluttered, redundant, or unstructured, your agent’s answers will reflect that. This is where data pre-processing becomes crucial. By preparing your data carefully, you’re setting the foundation for high-quality, meaningful, and accurate responses.
In this series, we’ll guide you through everything you need to know to get your files and data ready for RAG. We'll cover:
- How to structure your documents for clarity,
- Best practices for cleaning and simplifying text,
- Adding metadata and summaries for richer context,
- Optimizing non-text data, like images and tables,
- Data validation and maintenance.
Each video will break down these steps with examples, giving you actionable insights to apply directly to your AI projects. By the end of this series, you'll have the tools to take any dataset, transform it for RAG, and optimize the performance of your AI agents.
