What if building a custom AI model for your business was as simple as giving feedback—no massive labeled datasets required? In this episode, we sit down with Travis Addair, CTO and Co-Founder of Predibase, creators of the first reinforcement fine-tuning platform, to explore the future of specialized AI.

Discover how reinforcement fine-tuning is revolutionizing model customization, enabling you to start fast, adapt to your unique data, and keep improving through human feedback. Whether you’re an AI enthusiast or a business leader, you’ll learn how this breakthrough is making advanced AI accessible to everyone.

Highlights:

• How reinforcement fine-tuning simplifies building custom models
• The impact of human feedback on continuous model improvement
• Making advanced AI accessible with minimal labeled data

In this episode, Pallavi Koppol, Research Scientist at Databricks, explores the importance of domain-specific intelligence in large language models (LLMs). She discusses how enterprises need models tailored to their unique jargon, data, and tasks rather than relying solely on general benchmarks.

Highlights include:
- Why benchmarking LLMs for domain-specific tasks is critical for enterprise AI.
- An introduction to the Databricks Intelligence Benchmarking Suite (DIBS).
- Evaluating models on real-world applications like RAG, text-to-JSON, and function calling.
- The evolving landscape of open-source vs. closed-source LLMs.
- How industry and academia can collaborate to improve AI benchmarking.

In this episode, Kilian Lieret, Research Software Engineer, and Carlos Jimenez, Computer Science PhD Candidate at Princeton University, discuss SWE-bench and SWE-agent, two groundbreaking tools for evaluating and enhancing AI in software engineering.

Highlights include:
- SWE-bench: A benchmark for assessing AI models on real-world coding tasks.
- Addressing data leakage concerns in GitHub-sourced benchmarks.
- SWE-agent: An AI-driven system for navigating and solving coding challenges.
- Overcoming agent limitations, such as getting stuck in loops.
- The future of AI-powered code reviews and automation in software engineering.