Prompt Caching: 10x Cheaper LLM Tokens
This article discusses a technique called 'prompt caching' that can significantly reduce the cost of using large language models (LLMs) by reusing previous responses.
Why it matters
Prompt caching is an important optimization that can dramatically reduce the operational costs of deploying large language models in production.
Key Points
- 1Prompt caching allows reusing previous LLM responses, reducing token usage by up to 10x
- 2The technique involves storing and retrieving cached responses based on the input prompt
- 3Caching can be applied to various LLM use cases like chatbots, content generation, and code completion
Details
Prompt caching is a technique that can dramatically reduce the cost of using large language models (LLMs) by reusing previous responses. LLMs like GPT-3 and Anthropic's Claude charge based on the number of tokens (words) generated, so minimizing token usage is crucial for cost-effective deployment. Prompt caching works by storing the input prompt and the corresponding LLM response, then checking the cache before sending a new request to the model. If a matching prompt is found, the cached response can be returned instead of generating a new one, reducing token usage by up to 10x. This technique can be applied to various LLM use cases like chatbots, content generation, and code completion. While it requires additional infrastructure to implement the caching system, the significant cost savings make it an attractive optimization for companies and developers working with LLMs.
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