Token Count and AI Model Capabilities

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The chapter explores the importance of token count in AI models such as GPT 3.5 Turbo, GPT 4 Turbo, and Clod, discussing how it impacts visibility window and context maintenance. It also touches on input length variations, Gemini 1.5 Pro features, and API documentation generation based on a database schema.

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.