World's Fair 2025
Forget RAG Pipelines—Build Production Ready Agents in 15 Mins: Nina Lopatina, Rajiv Shah, Contextual
Overview
This talk introduces Contextual AI's platform for building production-ready Retrieval Augmented Generation (RAG) agents quickly, aiming to simplify the RAG pipeline. The core thesis is that RAG can be treated as a managed service, abstracting away the complexities of building and maintaining individual components like vector databases and LLM training. The platform offers an end-to-end solution for ingesting data, retrieving relevant information, and generating grounded responses, with a focus on enterprise-grade accuracy and modularity.
Who should watch
- AI Engineers looking to build and deploy RAG systems efficiently.
- Product Managers seeking to understand the capabilities and development speed of RAG agents.
- Builders who want to integrate RAG into existing applications or workflows.
- Developers struggling with the complexity of custom RAG pipeline development.
- Those interested in advanced RAG features like custom extraction, reranking, and grounded generation.
Key takeaways
- RAG can be managed as a service, eliminating the need for developers to build and maintain complex individual pipelines.
- The Contextual platform provides an end-to-end solution for RAG, from data ingestion and parsing to retrieval and grounded generation.
- The platform emphasizes modularity, allowing users to leverage individual components like their reranker or grounded language model.
- Contextual's grounded language model is specifically fine-tuned to respect provided context and avoid generating information not present in the source documents.
- The platform offers advanced features for document understanding, including specialized parsing for tables and images, and supports hybrid search with BM25 and embedding models.
- Evaluation is facilitated through natural language unit tests using an LM-unit model, providing scores for accuracy, causation, synthesis, and more.
- RAG agents can be integrated with other tools and clients, such as Claude desktop via MCP, enabling broader application use.
- The platform supports both no-code and developer-focused interfaces, catering to different user needs and technical expertise.
Notable quotes
*We can treat Rag like a managed service.*
*It's easy to build that demo with 10 documents when you're doing rag. But when you go to scale that out, all of a sudden it's hard.*
*We want a large language model that's been specifically fine-tuned to respect the knowledge that it's given not give its own knowledge.*
Unofficial community note. Prefer the recording for nuance.