So you have implemented a RAG solution, and maybe you even put up evaluations around it. Great! But once you have those things in place, what if your evaluations tell you your solution is not cutting it? Where are the knobs you can turn and levers you can pull in your AI application that are going to make the difference?

Join me as we walk through the 3 main areas changes can be made in a RAG solution to change your outcomes.
- Document Ingestion
- Document Retrieval
- Inference

Talk focuses on the Document Retrieval stage and highlights 3 techniques that can be useful, as well as when you may want to use them:
- Hybrid Search
- Reranking
- Self-Querying Retrievers

Live code examples establishing a baseline, as well as examples of all 3 of these techniques both independently and combined are presented. These examples show how they are put together, as well as results leveraging an open dataset of movie synopses.

- 50 minute session or longer preferred

- Live code examples are easy to follow, but require some familiarity with RAG or python or both.

- Uses this github repo as both a template for the talk, and something people can take home and try for themselves: https://github.com/grey-lovelace/ai-iowa-make-rag-better