Anthropic has launched a new feature on its API called prompt caching, now available in public beta for the Claude 3.5 Sonnet and Claude 3 Haiku models, with support for Claude 3 Opus on the way. Prompt caching allows developers to store frequently used prompt contexts, enabling more efficient API calls by reducing costs by up to 90% and latency by up to 85% for long prompts.
Table of Contents
Key Benefits and Use Cases
Prompt caching is particularly useful in scenarios where the same context or instructions are repeatedly needed across multiple interactions. Here’s how it can enhance various applications:
- Conversational Agents: By caching long instructions or uploaded documents, conversational agents can handle extended dialogues with lower costs and reduced latency.
- Coding Assistants: Cache a summarized version of a codebase to improve code autocomplete and Q&A functionality, allowing Claude to respond quickly without reloading extensive data.
- Large Document Processing: Incorporate full-length documents, including images, into your prompts while minimizing response time, making it easier to process and query extensive materials.
- Detailed Instruction Sets: Share comprehensive lists of procedures and examples with Claude. Prompt caching enables the inclusion of multiple high-quality examples, which can fine-tune Claude’s responses more effectively.
- Agentic Search and Tool Use: For workflows involving multiple API calls, caching intermediate results can significantly speed up the process by reducing the need for redundant data transmission.
- Long-Form Content Interaction: Embed entire books, papers, or transcripts into the prompt cache, allowing users to query detailed information efficiently.
Early Feedback and Pricing
Early users of prompt caching have reported substantial improvements in both speed and cost efficiency. This feature has been particularly beneficial for tasks requiring extensive context, such as embedding a full knowledge base or using numerous examples to guide model outputs.
Pricing Structure:
- Writing to Cache: Writing content to the cache costs 25% more than the base input token price for the model in use.
- Using Cached Content: Once cached, using this content in subsequent calls is much cheaper, costing only 10% of the base input token price.
With prompt caching, developers can make Claude models more responsive and cost-effective, especially in use cases involving large and complex datasets. This feature is set to further enhance the capabilities of AI applications, making them faster and more efficient while reducing overall costs.
