In a significant leap forward for AI technology, Anthropic has unveiled prompt caching for its Claude AI models. This innovative feature promises to revolutionize how developers and businesses interact with large language models, offering substantial improvements in both performance and cost-efficiency.
What is Prompt Caching?
Prompt caching allows developers to store and reuse frequently used context between API calls. This means that large amounts of background information, instructions, or example outputs can be provided to Claude without the need to reprocess this data with each new request.
Key Benefits
- Cost Reduction: Prompt caching can slash costs by up to 90% for long prompts.
- Improved Latency: Response times can be reduced by up to 85%, particularly for queries involving extensive context.
- Enhanced Performance: By allowing more comprehensive background information and examples, Claude can provide more accurate and tailored responses.
According to Anthropic's documentation, these improvements have been observed across various use cases:
| Use case | Latency w/o caching | Latency w/ caching | Cost reduction |
|---|---|---|---|
| Chat with a book (100,000 token cached prompt) | 11.5s | 2.4s (-79%) | -90% |
| Many-shot prompting (10,000 token prompt) | 1.6s | 1.1s (-31%) | -86% |
| Multi-turn conversation (10-turn convo with a long system prompt) | ~10s | ~2.5s (-75%) | -53% |
Use Cases
Prompt caching is particularly beneficial for:
- Conversational agents with long-running dialogues
- Coding assistants that need to reference large codebases
- Processing and querying large documents
- Implementing detailed instruction sets
- Agentic search and tool use scenarios
- Creating interactive experiences with books, papers, or other long-form content
Pricing Structure
Anthropic has introduced a nuanced pricing model for prompt caching:
- Writing to the cache costs 25% more than the base input token price
- Using cached content is significantly cheaper, at only 10% of the base input token price
This structure incentivizes efficient use of the caching feature while providing substantial cost savings for frequent API users.
Implementation Example
Here's a simple example of how to implement prompt caching using the Anthropic API with Python:
import anthropic
client = anthropic.Anthropic()
# The text of "Pride and Prejudice" would be stored in this variable
book_text = "It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife. ..."
response = client.messages.create(
model="claude-3-sonnet-20240229",
max_tokens=1000,
temperature=0,
messages=[
{
"role": "system",
"content": "You are an expert on Jane Austen's 'Pride and Prejudice'. Answer questions about the book based on the full text provided.",
"cache_control": "keep"
},
{
"role": "user",
"content": book_text,
"cache_control": "keep"
},
{
"role": "user",
"content": "Who are the main characters in the book?"
}
]
)
print(response.content)
In this example, the system message and the book text are marked with cache_control: "keep", indicating that they should be cached for future use. Subsequent API calls can reuse this cached content, reducing costs and latency.
Availability and Implementation
Prompt caching is currently available in public beta for Claude 3.5 Sonnet and Claude 3 Haiku, with support for Claude 3 Opus coming soon. Developers can easily implement this feature using the cache_control parameter in their API calls, as demonstrated in the code example above.
For more detailed information and best practices, Anthropic has provided a prompt caching cookbook with additional examples and guidance.
Industry Impact
The introduction of prompt caching is already making waves in the tech industry. Notion, a popular productivity tool, is integrating this feature into its AI-powered capabilities, anticipating significant improvements in speed and cost-effectiveness for its users.
Simon Last, Co-founder at Notion, expressed enthusiasm about the new feature:
"We're excited to use prompt caching to make Notion AI faster and cheaper, all while maintaining state-of-the-art quality."
Looking Ahead
As AI continues to evolve, features like prompt caching demonstrate the ongoing efforts to make these powerful tools more accessible and efficient. By addressing key challenges of cost and latency, Anthropic is paving the way for more widespread adoption and innovative applications of AI technology.
For developers and businesses looking to leverage this new capability, Anthropic provides comprehensive documentation and examples to help integrate prompt caching into existing workflows. As the AI landscape continues to evolve, prompt caching represents a significant step forward in making large language models more practical and cost-effective for a wide range of applications.
