Zain Hasan - Advanced Retrieval-Augmented Generation Techniques

Chatbots are becoming increasingly popular for interacting with users, providing information, entertainment, and assistance. However, building chatbots that can handle diverse and complex user queries is still a challenging task. One of the main difficulties is finding relevant and reliable information from large and noisy data sources.
In this talk, I will present some of the latest advances in retrieval-augmented generation(RAG) techniques, which combine the strengths of both retrieval-based and generative approaches for chatbot development. Retrieval-based methods can leverage existing text documents to provide informative and coherent responses, while generative methods can produce novel and engaging conversations personalized to the user.
I will cover the following topics:
1. Hybrid search with vector databases: How to use both keyword-based and semantic-based search methods to retrieve relevant documents from large-scale vector databases.
2. Query generation using LLMs: How to use large language models to generate natural and effective queries for document retrieval, based on the user input and the dialogue history.
3. Automatically excluding irrelevant search results: How to use various filtering and ranking techniques based on vector distance to exclude irrelevant search results.
4. Re-ranking: How to dynamically re-rank retrieved documents to further improve context relevance.
5. Chunking Techniques: How to use text segmentation and summarization methods to chunk long documents into shorter and more relevant passages.
I will demonstrate the effectiveness of these advanced techniques in the RAG workflow. I will also discuss the challenges and limitations of these techniques and the future directions for research and development.
Speakers: Zain Hasan
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[@tristanreid5770]

Interesting! I've never seen that paper about retrieval granularity before, the idea of 'proposition-izing' the text is in some ways more compelling to me than knowledge-graph approaches for pre-processing docs. One drawback could be that it adds another opportunity for errors to be inserted into your context, but conversely it's also a transformation that can be corrected if errors are detected, so over time the corpus of propositions become more valuable.

[@juansebastian6284]

Are the slides publicly available?