What are the limitations of general large language models, and when should you evaluate more specialized models for your team’s most important use case?

This week, Conor Bronsdon sits down with Brandon Jung, Vice President of Ecosystem at Tabnine, to explore the difference between specialized models and LLMs. Brandon highlights how specialized models outperform LLMs when it comes to specific coding tasks, and how developers can leverage tailored solutions to improve developer productivity and code quality. The conversation covers the importance of data transparency, data origination, cost implications, and regulatory considerations such as the EU's AI Act.

Whether you're a developer looking to boost your productivity or an engineering leader evaluating solutions for your team, this episode offers important context on the next wave of AI solutions
Topics:

00:31 Specialized models vs. LLMs
01:56 The problems with LLMs and data integrity
12:34 Why AGI is further away than we think
16:11 Evaluating the right models for your engineering team
23:42 Is AI code secure?
26:22 How to adjust to work with AI effectively 32:48 Training developers in the new AI world

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How Specialized Models Drive Developer Productivity | Tabnine’s Brandon Jung

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