Contextual AI with Amanpreet Singh - Weaviate Podcast #114!
Feb 12, 2025
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Amanpreet Singh, Co-Founder and CTO of Contextual AI, dives into the revolutionary world of Retrieval Augmented Generation (RAG) 2.0. He discusses the seamless integration of generative and retrieval models and the challenges of prompt engineering. Amanpreet emphasizes the necessity of continual learning and updates to model weights to resolve knowledge conflicts. The conversation also highlights the potential of reinforcement learning algorithms and the importance of domain-specific data. Buckle up for insights on the future of AI and specialized agents!
The transition to RAG 2.0 is pivotal, integrating retriever and generator models for improved accuracy and efficiency in AI systems.
Continual learning and active retrieval mechanisms are essential for AI models to adapt dynamically based on real-time user feedback.
Future AI systems must prioritize specialization to effectively meet enterprise-specific needs while refining their operations through interaction and feedback.
Deep dives
The Genesis of Contextual AI
Contextual AI was founded to address the reliability issues in production-grade AI applications, particularly following the rise of AI tools like ChatGPT. The team, with over a decade of experience in deep learning, identified the significant gap between the capabilities of AI technology and its application in real-world scenarios, particularly in production environments. They recognized that while models can perform well in controlled conditions, they often fail when faced with production-level challenges due to issues like prompt engineering. Contextual AI aims to create systems that can automatically optimize themselves over time to achieve the necessary level of accuracy and trustworthiness required for enterprise deployment.
RAG 2.0: Optimizing AI Retrieval Systems
The podcast discusses the evolution of Retrieval-Augmented Generation (RAG) systems, emphasizing the transition to RAG 2.0. RAG 2.0 improves upon traditional retrieval systems by integrating the retriever and generator into a unified model that can learn and optimize end-to-end. This system enhances the retriever's ability to return relevant information, allowing the generator to produce accurate outputs, effectively minimizing the garbage-in-garbage-out problem associated with previous models. The integration of active retrieval techniques enables the model to refine its queries based on previous outputs, demonstrating a more dynamic and responsive approach to AI.
Active Retrieval and Improved User Interaction
Contextual AI has implemented active retrieval, allowing its models to adapt continuously based on real-time feedback. This paradigm shift enables AI models to decide on needed information dynamically and can initiate multiple retrieval calls to improve response accuracy. By leveraging each interaction and the user's feedback, such as asking for clarifications or additional information, the models are designed to enhance their effectiveness over time. This innovation marks a departure from earlier static systems by ensuring that the AI can grow and adapt alongside the enterprise's specific needs.
Feedback Loops and Continuous Learning
The discussion highlights the critical role of feedback loops in enhancing the performance of AI systems in real-world applications. Contextual AI employs a combination of user preferences and machine learning techniques to optimize how AI responds to various queries. By allowing for preferences such as recency or specific document authority within a company's knowledge base, the system can learn to prioritize the most relevant information. This process is crucial for ensuring that the AI system can maintain high levels of accuracy and reliability in providing information.
Future Directions: Specialization and Integration
The podcast concludes with insights into future trends in AI, particularly the importance of specialization in enterprise applications. Contextual AI emphasizes that general agents need to learn and adapt to the specific environments they operate within, which cannot solely depend on trained data but rather require ongoing interaction feedback. Additionally, as the field evolves, the integration of various model types could enable more efficient processing and problem-solving capabilities. The pursuit of refining AI agents to become more aligned with human actions continues to be a focal point for future development.
Hey everyone! Thank you so much for watching the 114th episode of the Weaviate Podcast featuring Amanpreet Singh, Co-Founder and CTO of Contextual AI! Contextual AI is at the forefront of production-grade RAG agents! I learned so much from this conversation! We began by discussing the vision of RAG 2.0, jointly optimizing generative and retrieval models! This then lead us to discuss Agentic RAG and how the RAG 2.0 roadmap is evolving with emerging perspectives on tool use. Amanpreet continues to further motivate the importance of continual learning of the model and the prompt / few-shot examples -- discussing the limits of prompt engineering. Personally I have to admit I think I have been a bit too bullish on only tuning instructions / examples, Amanpreet made an excellent case for updating the weights of the models as well -- citing issues such as parametric knowledge conflicts, and later on discussing how Mechanistic Interpretability is used to audit models and their updates in enterprise settings. We then discussed Contextual AI's LMUnit for evaluating these systems. This then lead us into my favorite part of the podcast, a deep dive into RL algorithms for LLMs. I highly recommend checking out the links below to learn more about Contextual's innovations on APO and KTO! We then discuss the importance of domain specific data, Mechanistic Interpretability, return to another question on RAG 2.0, and conclude with Amanpreet's most exciting future directions for AI! I hope you enjoy the podcast!
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