Jason Liu, Co-founder of Instructor, discusses the evolution of recommendation frameworks with GPT-3 embeddings at Stitch Fix, the importance of function calling for structured outputs, optimizing workflows for model fine-tuning, the role of prompts in AI models, and exploring hiring practices and innovation in AI engineering.

Jason Liu, a freelance AI consultant and creator of the Instructor library, dives deep into retrieval-augmented generation (RAG) systems. He shares key signs of RAG system failures and the tactical steps to diagnose issues. Liu emphasizes building robust test datasets and the importance of data-driven experimentation. He discusses fine-tuning strategies, chunking techniques, and collaboration tools, while also showcasing how future AI models could revolutionize the space. Finally, he highlights his passion for teaching through his AI consulting course.

Jason Liu, an independent consultant in recommendation systems, discusses building AI apps, playbook for ML, and avoiding pitfalls. They focus on building terrible AI systems to learn how to prevent failures. The podcast explores consulting in various industries, future tooling, and creating robust AI systems.

Jason Liu, a seasoned AI operator and consultant, discusses general applications vs. AI applications, how developers should work with LLMs, what LLMOps companies need to focus on, why saying 'no' can improve AI's success rate, building agile AI systems, best practices for RAG systems, and more!

Jason Liu, an independent AI consultant with a background at Meta and Stitch Fix, joins the discussion. He shares insights into structuring valuable consulting contracts and shifting from hourly billing to larger deals. Engaging in a live role-play, Jason coaches the host on effective client interaction and pricing strategies. The conversation also highlights the shift from deterministic to probabilistic AI systems, emphasizing the importance of understanding client motivations and fostering meaningful relationships in the evolving freelance landscape.

Jason Liu, a Renaissance Man in AI, discusses strategies for building valuable AI products. Topics include structuring LLM outputs, automating research, aligning metrics with outcomes, improving recommendation systems, and the future of AI. They explore challenges in building recommender systems, data validation, fine-tuning Python functions, and enhancing AI systems performance and interpretability.

Machine learning expert Jason Liu discusses working with LLMs, shipping them to production, making them accessible. Talks about future of prompt engineering, building better prompts, AI industry evolution, prompt engineering nuances, on-device models, model training advancements, maximizing value with AI products, V0 .dev tool tip, voice coding tools, text effects CLI library, Chidori framework, Dagger integration, One Sec app for productivity

Jason Liu, a former data scientist at StitchFix and Meta, dives into the world of Retrieval Augmented Generation (RAG) and its transformative effects on AI tools. He discusses the shift toward smaller teams and specialized businesses in AI, as well as the nuances of AI consulting. Jason emphasizes the importance of clear communication in writing, the role of research in AI, and how innovations like micro apps can monetize creativity. His insights highlight the potential of AI to enhance productivity while navigating challenges in an evolving landscape.

Experts discuss practical applications of Large Language Models in customer service, property management, and paper summarization. They explore AI's impact on product development, the importance of clear communication, and maximizing workflow efficiency for revenue growth.

Jason Liu, an applied AI consultant and creator of Instructor, discusses challenges and applications of LLMs. Topics include making LLMs interact with existing systems, building applications with LLMs, thinking in logic and design, and the future of Instructor. They also explore misconceptions in LLMs, improving LLM applications, RAG as recommendation systems, fine-tuning embedding models, measuring impact on business outcomes, and unlocking economic value through structured data extraction.