World's Fair 2025
The State of AI Powered Search and Retrieval — Frank Liu, MongoDB (prev Voyage AI)
Overview
This talk explores the evolution and future of AI-powered search and retrieval, moving beyond traditional keyword matching to understand user intent and conceptual relationships. It highlights how AI search systems can provide more grounded and relevant responses, particularly in applications like Retrieval-Augmented Generation (RAG), by leveraging embeddings and advanced techniques. The discussion also touches upon the increasing importance of multimodality and agentic capabilities in shaping the next generation of search technologies.
Who should watch
- AI Engineers
- Product Managers
- Builders working with search and retrieval systems
- Those interested in RAG, embeddings, and LLM applications
- Developers looking to enhance search functionality in their products
Key takeaways
- AI-powered search goes beyond keyword matching to understand user intent and find related concepts, even without identical wording.
- Retrieval-Augmented Generation (RAG) significantly improves LLM responses by grounding them in relevant retrieved information, preventing hallucinations or generic answers.
- Embedding quality is a core component of AI-powered search, with most systems utilizing embeddings to represent and search unstructured data like text documents.
- There is no one-size-fits-all embedding model; evaluations are crucial to determine the best model for specific applications, such as code-related tasks.
- Powerful search and retrieval systems often require more than just embeddings; integrating structured data and filtering capabilities is essential.
- Agentic retrieval systems are evolving into feedback loops where LLMs can expand or decompose queries, leading to more sophisticated search interactions.
- The future of AI search is multimodal, enabling systems to understand and embed combinations of text, images, and audio.
- Instruction tuning and reasoning capabilities will allow for more steerable embeddings, enabling searches that focus on specific aspects of a query.
Notable quotes
*AI powered search goes way beyond that not only it can understand keywords it can help you retrieve based on some of these more traditional information retrieval algorithms but it can also help you find related concepts to that point.*
*Always do evaluations to see which one is best for your application.*
*Oftentimes embeddings alone are not enough. If you want to build a really powerful search and retrieval system, you need to have a lot of that structured data that's a part of it.*
Unofficial community note. Prefer the recording for nuance.