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Testμ 2024
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Eran Yahav

Eran Yahav

CTO, Tabnine

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How to Use Personalized AI Agents to Speed Up Software Development & Improve Code Quality

About the Talk

AI code assistants are becoming an expected part of every developer’s tool chain. But not everyone is seeing real benefits from them and concerns persist about the quality of what they generate. In this session, you'll discover practical ways to make AI tools more relevant to your specific needs, significantly enhancing developer productivity and code quality. The session includes the following highlights:

The role of Retrieval-Augmented Generation (RAG):

Learn how Retrieval-Augmented Generation (RAG) works and how it improves the output of artificial intelligence. RAG ensures that AI responses are contextually rich and highly relevant by incorporating external information into the language model's prompt. By utilizing this technique, your AI code assistant can draw from a vast array of code elements and documentation, resulting in more accurate and valuable suggestions.

The importance of using your existing codebase as context:

Learn how codebase awareness (both within your IDE and connections to your global codebase) can dramatically improve the performance of AI assistants and improve your development process. Discover how developers can receive precise, context-aware suggestions by leveraging the variables, developer comments, open files, imported packages, and active projects accessible from the IDE. See how integrating your AI tools with code repositories, design documents, and issue trackers can dramatically increase the relevance and accuracy of code recommendations, explanations, and generated tests. Learn how to adapt AI behavior to your organization's standards by guiding and customizing it.

Practical guidance on the best way to customize and fine-tune AI coding agents for your specific needs

Practical advice on how to customize and fine-tune AI coding agents to meet your specific needs. Take actionable steps to incorporate AI into your engineering organization and ensure they provide the most relevant and useful assistance without compromising your data privacy and security.

Key Takeaways:

  • How to customize AI code assistants like Tabnine and other AI-enabled tools to best suit your organization's needs
  • Why context and RAG are critical components of successful use of generative AI tools
  • How to harness codebase awareness to improve AI output
  • How to provide explicit coaching to your AI tools.

About the Speaker

Eran Yahav is the CTO of Tabnine and a professor of Computer Science at the Technion, Israel. His research interests include program synthesis, machine learning for code, program analysis, and program verification. Eran loves long-distance running, and while he hasn’t yet won any medals, he has suffered at least one instance of heatstroke while trying..

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