Understanding Prompt Caching and Implementing it with Anthropic API

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Prompt Caching

A prompt is an integral part of any Gen AI application. However, the problem occurs when the prompt becomes too large.

As then we start incurring more cost. What if we can cache the prompt to save on the cost?

Let us understand the concept of Prompt Caching in simple language in this article

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Why Do We Need Prompt Caching?

Prompts play a crucial role in generative AI applications, but they can sometimes become excessively large. This happens in two ways:

1. The prompt itself is very large.
2. We send too much additional data along with the prompt.

In both cases, the size of the input increases, leading to higher costs and longer processing times. What if we could cache the prompts to avoid redundant computations and token usage? That’s where prompt caching comes into play.

How Prompt Caching Works

Instead of repeatedly sending the same prompt, we can store it in memory. When a request is made, the system first checks if the prompt is already cached.

If found, it retrieves the cached data, reducing the input token count and making the process faster and more cost-efficient.

Implementing Prompt Caching with Anthropic API

To implement prompt caching, we can use Anthropic’s API.

You can find the code notebook here – https://github.com/tayaln/Prompt-Caching

Below are the steps:

Step 1 – Install Anthropic API:

First, sign up on Anthropic’s website, create an account, and add at least $5 to access the API.

Use Claude 3.5 Sonnet Model: In this example, we use the Claude 3.5 Sonnet model, released on October 22, 2024.

Step 2 – Load a Text File:

Upload a text file containing the input data and retrieve its path for processing.

Step 3 – Make an Uncached Request:

Send a request without caching and measure the time and token usage.

Step 4 – Enable Prompt Caching:

Use the cache control feature with type = ephemeral. This stores the prompt and retrieves it when needed, reducing redundant computations.

Performance Comparison: Cached vs. Uncached Requests

Cost Savings with Prompt Caching

• Writing tokens is 25% more expensive, but this cost is offset by 90% savings when reading cached tokens.
• Standard input/output tokens are priced normally, so caching is most effective when the same prompt needs to be used multiple times.

When to Use Prompt Caching

You don’t need to cache every prompt. Instead, use caching when:

• You frequently reuse the same prompt.
• You need to retrieve stored results multiple times.
• Your application requires optimized cost and performance.

Conclusion

Prompt caching is a simple yet powerful technique that improves the efficiency and cost-effectiveness of AI applications.

By leveraging Anthropic’s caching feature, you can significantly reduce token usage, decrease processing time, and optimize your AI model’s performance.

If you have any questions, feel free to reach out on LinkedIn – https://www.linkedin.com/in/nikhileshtayal/.

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