In the context of Gen AI, understanding tokens and how to optimize them is crucial for efficient and cost-effective usage. Here’s what you need to know:
What Are Tokens? #
Tokens are chunks of text that AI models read. In English, a token can be as short as a single character or as long as a word (e.g., “a” or “apple”). Spaces and punctuation are also considered tokens.
Why Do Tokens Matter? #
- Cost: You are billed based on the number of tokens processed. Both input and output tokens count towards this.
- Latency: The length of your output and hence the tokens, affect the time it takes the LLM to deliver your results.
- Length: Models have a maximum token limit. If a conversation exceeds this limit, you’ll need to truncate or shrink the text.
- Quality: The way you structure your tokens can affect the quality of the model’s response.
Cost and Latency #
Since they are most important, let’s talk about the money and time part of using Gen AI.
Cost: More details in your prompt mean better performance but also a higher price tag. OpenAI charges for both input and output tokens, and it can add up quickly.
Experimenting with prompts is pretty cheap and fast, but the real cost comes in when you’re running applications. For example, using GPT-4 with 10k tokens in input and 200 tokens in output will cost you $0.624 per prediction. Compare that to GPT-3.5-turbo, which is only $0.004 per prediction. Still, imagine if you’re making billions of predictions a day like DoorDash in 2021; it could cost you $40 million a day!
Latency: In LLMs, the Input tokens can be processed all at once, so they don’t affect the time much. But the length of the output does. Even a super short input and output can take around 500ms with gpt-3.5-turbo. If the output is over 20 tokens, it’s over a second.
It’s hard to pin down exactly what causes the latency, but it could be the model, networking, or just some inefficient engineering. The good news? It might get better exponentially.
And here’s a heads-up: The world of LLM applications is moving super fast. Costs and latency are changing all the time, so what’s true today might not be true tomorrow. It’s like trying to hit a moving target, so keep an eye on it!
Optimization Strategies #
- Understanding Token Count: Be aware of how many tokens are in your prompt and the response. This helps in managing costs and staying within the model’s limits.
- Choosing the Right Model: Different models have different token limits and costs. Select the one that fits your needs.
- Efficient Prompting: Design your prompts efficiently. Unnecessary tokens can increase costs and may reduce the quality of the response.
- Utilizing Parameters: Parameters like
max_tokenscan be used to control the length of the response.
Conclusion #
Tokens are a fundamental concept in Generative AI. Understanding how they work and how to optimize them can lead to more effective and efficient use of the tool. Whether you’re experimenting, developing a product, or just playing around, keeping tokens in mind will enhance your experience and make your exploration of AI more rewarding.