Automated Data Generation for Restaurants

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Using AI to automatically generate fake restaurant names, menu items, prices, and ingredient lists enables the creation of realistic-looking data without the need for manual ideation. This process eliminates the manual effort required when using tools like Faker for data generation and allows for quick testing of applications with a significant amount of realistic data.

Join Scott and CJ as they dive into the fascinating world of AI, exploring topics from LLM token sizes and context windows to understanding input length. They discuss practical use cases and share insights on how web developers can leverage larger token counts to maximize the potential of AI and LLMs.

Show Notes

00:00 Welcome to Syntax!
01:31 Brought to you by Sentry.io.
02:42 What is a token?
- Quizgecko GPT-4 Token Counter.
04:22 Context window sometimes called “max tokens”.
- OpenAI Platform Models.
- Claude Models.
10:42 Understanding input length.
11:59 Models + services with big token counts.
- Gemini Docs.
13:22 Generating open API documentation for a complex API.
17:29 Generating JSDoc style typing.
- Drop-In stolinski GitHub.
21:07 Generating seed data for a complex database.
- bytedash w3cj GitHub.
24:34 Summarizing 8+ hours of video.
29:35 Some things we’ve yet to try.
31:32 What about cost?
- Google AI for Developers Cost.