Token Limits in Prompts
By now, you know LLMs are the AI powerhouses trained on heaps of data, and prompts are what enable you to make the most out of them.
However, it’s important to learn that different LLMs have specific token limits that define their performance. Ideally, when you’re creating your prompt, you need to ensure that you’re not crossing these token limits. Let’s understand this concept quickly.
Token Limits: These dictate how many tokens an LLM can handle in one go.
Estimated Word Counts: This refers to the approximate number of words that can fit within a model’s token limit. It helps you gauge how much content you can generate or process.
If you try copy-pasting a long Wikipedia article (for example, that of Google), you’ll notice an error.
Think of token and word counts as your LLM's capacity. While tokens define the technical limit, estimated word counts translate this into a more human-understandable measure.
Why It Matters: Knowing the estimated word count helps you manage your input prompts and outputs more efficiently.
Comparative Analysis: Token and Estimated Word Counts in a Few Leading LLMs
While the foundational knowledge provided is adequate for course progression, further exploration of tokens is available in the documentation linked below.
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