Member-only story
AI’nt That Easy #2: 4 Approaches to Solve LLM Token Limit and Rate Limit Issues
Introduction:
In the realm of natural language processing, working with large documents poses a unique set of challenges. Two major obstacles often encountered are the token limit and rate limit issues imposed by language model APIs.
Token limit: LLMs can only process a certain number of tokens (words or subwords) at a time. This means that longer texts have to be split into smaller chunks, which can affect the coherence and quality of the generated text. The token limit depends on the model architecture and the available memory. For example, ChatGPT has a token limit of 2048, which is equivalent to about 1500 words or 6–8 paragraphs.
Rate limit: LLMs are expensive to run and require a lot of computational resources. Therefore, there is a limit on how many requests or tokens can be sent to the LLM per minute or per day. This is called the rate limit, and it varies depending on the pricing tier and the API provider. For example, OpenAI has a rate limit of 150,000 tokens per minute.
Large documents frequently surpass the token limits set by language models, which leads LLM to return an error message and stop generating text. This can be frustrating for the users who want to use the LLM for various applications.
