Webinar Recap: Retrieval Techniques for Accessing the Most Relevant Context for LLM Applications

In a recent webinar, Harrison Chase and Filip Haltmayer discussed retrieval techniques for accessing the most relevant context for large language model (LLM) applications. Retrieval involves extracting information from connected external sources and incorporating it into queries to provide context. Semantic search is one of the most critical use cases for retrieval, which functions within a typical CVP architecture (ChatGPT+Vector store+Prompt as code). The webinar also covered edge cases of semantic searches, such as repeated information, conflicting information, temporality, metadata querying, and multi-hop questions. Various solutions to these challenges were proposed during the discussion.