Aside from the hype around code generation, the potential of information retrieval, especially retrieval augmented generation (RAG), is often overlooked. Here, LLMs are becoming the industry standard for retrieving information from documents. Embedding Models allow the retrieval of information based on imprecise search terms in a way that was not possible with full text indices.
However, the application of RAG techniques on code bases is much less common. This talk sheds some light on the current state of retrieval augmented generation, how easy it is to use, and how it can empower development teams.
We will have a look at the general techniques, what online models are capable of, and what performance can be expected from local setups. We will explore the state of RAG support for Elixir, both as input-codebase, and as processing entity. We will further see what influence the coding style and the ingestion process has on the usefulness of the results.
Key Takeaways:
- At the end of the talk, the audience will know what benefits retrieval augmented generation (RAG) on code has for their team, and what performance to expect.
- They would have an idea on how to set up their own open source RAG system, and how to amend it to their needs.
- They would also be able to understand what uses cases RAG offers and spot it’s usage in the wild.
Target Audience:
- Developers who are interested in the influence and benefits of AI, specifically Retrieval-Augmented Generation (RAG), on their workflow and how it can enhance collaboration among team members.
- CTOs, Lead Developers, and Engineering Managers who aim to foster effective information exchange and improve the efficiency of their development teams.
- People interested in LLM’s, AI, RAG