# Enhancing The Abilities Of Software Engineers With Generative AI At Tabnine Page: https://stenobird.com/podcast/ai-engineering-podcast/enhancing-the-abilities-of-software-engineers-with-generative-ai-at-tabnine Text version: https://stenobird.com/podcast/ai-engineering-podcast/enhancing-the-abilities-of-software-engineers-with-generative-ai-at-tabnine.md Podcast: [AI Engineering Podcast](https://stenobird.com/podcast/ai-engineering-podcast) Published: 2023-11-13T02:00:00+00:00 Episode link: https://www.aiengineeringpodcast.com/tabnine-generative-ai-developer-assistant-episode-24 Audio file: https://op3.dev/e/dts.podtrac.com/redirect.mp3/serve.podhome.fm/episode/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/6385305391814178214a2ae2f2-3054-4952-a3aa-d0049cb51c4ev1.mp3 Processing state: failed JSON: https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/enhancing-the-abilities-of-software-engineers-with-generative-ai-at-tabnine Duration seconds: 3888 ## Resource Summary Software development involves an interesting balance of creativity and repetition of patterns. Generative AI has accelerated the ability of developer tools to provide useful suggestions that speed up the work of engineers. Tabnine is one of the main platforms offering an AI powered assistant for software engineers. In this episode Eran Yahav shares the journey that he has taken in building this product and the ways that it enhances the ability of humans to get their work done, and when the humans have to adapt to the tool. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Your host is Tobias Macey and today I'm interviewing Eran Yahav about building an AI powered developer assistant at Tabnine Interview Introduction How did you get involved in machine learning? Can you describe what Tabnine is and the story behind it? What are the individual and organizational motivations for using AI to generate code?  What are the real-world limitations of generative AI for creating software? (e.g. size/complexity of the outputs, naming conventions, etc.) What are the elements of skepticism/oversight that developers need to exercise while using a system like Tabnine? What are some of the primary ways that developers interact with Tabnine during their development workflow?  Are there any particular styles of software for which an AI is more appropriate/capable? (e.g. webapps vs. data pipelines vs. exploratory analysis, etc.) For natural languages there is a strong bias toward English in the current generation of LLMs. How does that translate into computer languages? (e.g. Python, Java, C++, etc.) Can you describe the structure and implementation of Tabnine?  Do you rely… ## Actions - request_transcript: `POST https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/enhancing-the-abilities-of-software-engineers-with-generative-ai-at-tabnine/transcription-requests` — Idempotently request low-priority transcript generation for this episode. - read_markdown: `GET https://stenobird.com/podcast/ai-engineering-podcast/enhancing-the-abilities-of-software-engineers-with-generative-ai-at-tabnine.md` — Read the agent-friendly Markdown representation of this episode resource. A page view does not enqueue transcription. Agents should invoke `request_transcript` explicitly when they need this episode processed. ## Transcript Full transcripts are not published on public pages unless there is a clear rights basis.