Is This the End of RAG? Anthropic's NEW Prompt Caching

Summary

Anthropic introduces prompt caching for CLA, a feature that significantly reduces costs by up to 90% and latency by up to 85%. This innovation allows developers to cache frequently used prompts, enhancing efficiency in long conversations and large document processing. The video explores prompt caching's impact on performance, compares it with Google's Gemini context caching, and discusses use cases, cost reductions, and best practices. It also addresses whether prompt caching can replace RAG, concluding that while it enhances capabilities, it's not a direct substitute.

Takeaways

• 😀 Anthropic introduced a new feature called 'prompt caching' which can significantly reduce costs by up to 90% and latency by up to 85%.
• 🔍 Google's Gemini models were the first to introduce context caching, but Anthropic's approach has some distinct differences and advantages.
• 💡 Prompt caching allows developers to cache frequently used content between API calls, which is particularly useful for long conversations or large document processing.
• 📈 The performance improvement varies based on use cases, with some applications seeing an 80% reduction in latency and 90% reduction in cost.
• 💼 Prompt caching is available for Cloud 3.5 Sonet and Cloud 3, with support for Cloud 3.0 Opus coming soon.
• 💬 Use cases for prompt caching include conversational agents, coding assistants, large document processing, and accessing long-form content like books and podcasts.
• 💰 The cost of cache tokens is only 10% of the base input token price, but there's a 25% overhead for writing to the cache for the first time.
• 🚀 Prompt caching is not a replacement for RAG (Retrieval-Augmented Generation), but it can complement it by allowing for the caching of documents and tool definitions.
• 🛠️ Best practices for effective caching include caching stable and reusable content, placing cache content at the beginning of the prompt, and strategically using cache breakpoints.
• 📝 The video provides a practical example of using prompt caching with a large text, demonstrating a significant reduction in latency from 22 seconds to 4 seconds for subsequent API calls.

Q & A

What is the main advantage of Anthropic's prompt caching feature?

Anthropic's prompt caching feature can significantly reduce costs by up to 90% and latency by up to 85%, making it extremely beneficial for developers working with large amounts of data or long conversations.

How does prompt caching work with long documents?

Prompt caching allows developers to cache frequently used contacts between API calls, which is particularly useful for long documents. Instead of sending the entire document with each prompt, it can be cached, reducing the cost and latency of subsequent interactions.

What are some use cases for prompt caching mentioned in the script?

Use cases for prompt caching include conversational agents, coding assistants, large document processing, detailed instruction sets, agentic search and tool usage, and accessing long-form content like books, papers, documentation, and podcast transcripts.

How does prompt caching differ from Google's context caching in Gemini models?

While both prompt caching and context caching aim to reduce costs and latency, they differ in their approach to caching. Prompt caching allows for caching of frequently used contacts between API calls, whereas context caching in Gemini models involves caching the context of the conversation.

What is the minimum cachable prompt length for Anthropic's Cloud 3.5 Sonet and Clot 3 Opus?

The minimum cachable prompt length for Anthropic's Cloud 3.5 Sonet and Clot 3 Opus is 1024 tokens.

What is the lifetime of the cached content in prompt caching?

The cached content in prompt caching has a lifetime of 5 minutes, refreshed each time the cached content is used.

How does the cost of caching tokens compare to input/output tokens in prompt caching?

Caching tokens cost only 10% of the base input token price, which is a significant reduction. However, writing to the cache costs about 25% more than the base input token price for any given model.

What is the difference in cost and latency when using prompt caching for different applications?

The reduction in cost and latency varies by application. For example, chatting with documents and using 100,000 tokens without caching takes about 12 seconds, but with caching, it's reduced to 2.4 or 2.5 seconds, which is an 80% reduction in latency and a 90% reduction in cost.

What are some best practices for effective prompt caching?

Effective prompt caching practices include caching stable reusable content, placing cache content at the beginning of the prompt for best performance, using cache breakpoints strategically, and regularly analyzing cache hit rates to adjust the strategy as needed.

How does prompt caching compare to context caching in terms of cost and performance for long conversations?

Prompt caching can be more cost-effective and performant for short to medium-length conversations due to its 5-minute cache lifetime and lower cost for cached tokens. However, for longer conversations that exceed the 5-minute window, context caching may be more suitable, as it allows for a longer cache duration without the need for frequent refreshes.