Explained: AI Tokens & Optimizing AI Costs

Understanding what AI tokens are is the key to understanding how to control costs on your AI assistants, provide good responses to your users, and scale effeciently.

In this video we go over what Tokens are and use Examples from Voiceflow to show you how to optimize them when using AI responses with GPT, Claude, or other AI models.

What are AI Tokens?
What are GPT Tokens?
How do I select he right AI model?

All of these questions are answered!

🎯 Key Takeaways for quick navigation:

00:00 🤖 What are AI Tokens
- AI tokens play a crucial role in controlling AI costs and scaling AI agents effectively.
- Tokens are used to determine the cost of AI models like GPT or CLA.
- Understanding the balance between input and output tokens is essential for cost optimization.
01:25 🧩 How Tokens Work in AI Models
- AI input tokens and output tokens contribute to the overall cost.
- Input tokens often outweigh output tokens in terms of quantity.
- Optimizing the balance between input and output tokens is key to cost-effective AI usage.
03:31 📊 Token Calculation in Voice Flow
- Voice Flow offers a set number of tokens for a specified price.
- Different AI models have different token costs.
- Users can choose models based on their specific use cases, balancing accuracy and cost.
05:52 ⚙️ Token Optimization Strategies
- Adjusting the chunk limit can control the number of tokens used in retrieval tasks.
- Choosing the right model for different conversation points can enhance accuracy while managing costs.
- Increasing response length and modifying input parameters can improve user satisfaction and reduce the need for human support.


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