Is This the End of RAG? Anthropic's NEW Prompt Caching
TLDRAnthropic introduces prompt caching with CLA, a feature that significantly reduces costs and latency by up to 90% and 85% respectively. This advancement challenges the relevance of RAG, as it allows for caching frequently used prompts, making it highly beneficial for conversational agents, coding assistants, and large document processing. Despite similarities with Google's Gemini models, the differences in approach and limitations, such as a 5-minute cache lifetime for Anthropic, suggest that prompt caching is not a complete substitute for RAG but rather a complementary tool that enhances the capabilities of large language models.
Takeaways
- 🆕 Anthropic has introduced prompt caching with CLA, a feature that can significantly reduce costs and latency in AI model interactions.
- 💡 Prompt caching can save up to 90% on costs and reduce latency by up to 85%, making it a valuable tool for developers working with large prompts.
- 🔍 The feature is particularly useful for long-form conversations, coding assistants, large document processing, and detailed instruction sets.
- 📈 The performance benefits of prompt caching include substantial reductions in both cost and latency, varying based on the application and token usage.
- 📚 Prompt caching is available for Cloud 3.5 Sonnet and Clap 3, with support for Cloud 3.0 Opus coming soon.
- 🔑 Key differences exist between Anthropic's prompt caching and Google's Gemini models' context caching, particularly in terms of token limits and storage costs.
- 💻 The implementation of prompt caching requires adding a cache control block in API calls and adjusting the header of API requests.
- 📈 The cost of using the cache is reduced to 10% of the base input token price, but writing to the cache incurs a 25% overhead compared to the base input token price.
- 🕒 A limitation of prompt caching is the 5-minute lifetime for cached content, after which it must be refreshed, unlike Gemini's more flexible time-to-live settings.
- 📝 Best practices for effective caching include caching stable and reusable content, placing cache content at the beginning of prompts, and strategically using cache breakpoints.
- 🚫 Despite the benefits of prompt caching, it is not a replacement for RAG (Retrieval-Augmented Generation) in enterprise settings where extensive knowledge bases are involved.
Q & A
What is the new feature introduced by Anthropic that could potentially reduce costs and latency significantly?
-Anthropic introduced prompt caching with CLA (Context Length of 200,000 tokens), which can reduce costs by up to 90% and latency by up to 85%.
How does prompt caching work with Anthropic models in the context of long documents?
-Prompt caching allows developers to cache frequently used contexts between API calls. This is particularly useful when dealing with long documents, as they can be cached and not sent with each prompt, reducing costs and latency.
What are the performance differences one can expect with prompt caching?
-With prompt caching, for example, chatting with documents and sending 100,000 tokens without caching would take about 12 seconds, but with caching, it reduces to approximately 2.4 or 2.5 seconds, which is an 80% reduction in latency and a 90% reduction in cost.
What are some use cases for prompt caching according to the video script?
-Use cases for prompt caching include conversational agents with substantial chat history, coding assistants with large code bases, large document processing, detailed instruction sets, agentic search and tool usage, and engaging with long-form content like books, papers, and podcasts.
How does the cost structure of prompt caching tokens differ from input/output tokens?
-Prompt caching tokens cost only 10% of the base input token price, which significantly reduces the cost. However, writing to the cache costs about 25% more than the base input token price for any given model, introducing an overhead for the first-time write.
What is the difference between prompt caching by Anthropic and context caching by Google's Gemini models?
-The main differences lie in the minimum cachable prompt length and the cost structure. Anthropic allows a minimum of 1024 tokens for caching, while Gemini requires a minimum of 32,000 tokens. Additionally, Gemini has a storage cost associated with context caching, whereas Anthropic has a higher cost for writing to the cache but no storage cost.
What is the lifetime of the cache content in Anthropic's prompt caching?
-The cache content in Anthropic's prompt caching has a lifetime of 5 minutes, refreshed each time the cache content is used. If not used within 5 minutes, the content must be cached again.
How can prompt caching be combined with RAG (Retrieval-Augmented Generation) systems?
-While prompt caching is not a replacement for RAG, it can be used in conjunction with it. Long contexts from prompt caching can help RAG systems by allowing the models to create better answers by retrieving whole documents instead of chunks.
What are some best practices for effective caching mentioned in the video script?
-Best practices include caching stable reusable content like system instructions, background information, large contexts, and frequent definitions. Placing context cache content at the beginning of the prompt for best performance and using cache breakpoints strategically to separate different cachable prefix sections.
How does the API reference documentation for prompt caching differ from normal API calls?
-For prompt caching, an additional cache control block is required in the API call, and specific code must be included in the header of the API requests. There is also a beta API endpoint for prompt caching or the normal Anthropic API client can be used with the appropriate feature flag.
Can prompt caching replace RAG in all scenarios?
-No, prompt caching is not a complete replacement for RAG, especially in enterprise settings where knowledge bases span millions of tokens. The whole knowledge base is needed for effective retrieval, which is beyond the scope of prompt caching's 5-minute window and token limitations.
Outlines
🚀 Introduction to Anthropic's Prompt Caching
Anthropic has introduced a new feature called prompt caching with CLA that significantly reduces costs and latency in AI interactions. The feature allows caching of frequently used contacts, which can cut expenses by up to 90% and decrease response times by 85%. This is a major advancement in comparison to Google's Gemini models, which first introduced context caching. The video will explore the differences between the two approaches, demonstrate how to implement prompt caching, and discuss the performance improvements that can be expected. The feature is currently available for Cloud 3.5 Sonet and Cloud 3, with support for Cloud 3.0 coming soon. Use cases include conversational agents, coding assistants, large document processing, and more, with a table provided to show expected reductions in cost and latency.
🔑 API Reference and Key Differences with Gemini
The script delves into the API reference for prompt caching, noting that it is still in beta and subject to change. It highlights the key differences between Anthropic's prompt caching and Google's Gemini context caching, particularly the token limits for caching. While Anthropic allows a minimum of 1024 tokens for Cloud 3.5 and 248 tokens for Cloud 3.0, Gemini requires a minimum of 32,000 tokens. Additionally, Anthropic's cache has a 5-minute lifetime, refreshed with each use, whereas Gemini allows for a more flexible time-to-live setting at the cost of storage fees. The video also discusses best practices for effective caching and the process for making API calls with the new caching feature.
📚 Practical Examples and Use Cases for Prompt Caching
This section provides practical examples of how prompt caching can be applied, such as caching large contexts, tool definitions, and continuing multi-turn conversations. It demonstrates how to cache the content of a book like 'Pride and Prejudice' for quick retrieval in subsequent API calls, which can drastically reduce latency. The example shows a reduction from 22 seconds in a non-cached API call to just 4 seconds with caching. The script also explains how to set up the API calls with cache control blocks and headers, and how to monitor cache hit rates.
🤖 Comparison with Rag and Conclusion
The final paragraph discusses the comparison between prompt caching and the use of Rag (Retrieval-Augmented Generation) in enterprise settings. It argues that while prompt caching with long contexts can enhance the performance of Rag systems, it is not a replacement due to the limitations in token count and time window for caching. The script emphasizes that for knowledge bases spanning millions of tokens, the entire knowledge base may need to be embedded and stored for effective retrieval, which is beyond the scope of prompt caching. The video concludes by inviting viewers to share their thoughts on the topic and expresses gratitude for watching.
Mindmap
Keywords
💡Prompt Caching
💡Anthropic
💡Retrieval-Augmented Generation (RAG)
💡RAGCache
💡Claude 3
💡Context Caching
💡Latency
💡Cost Reduction
💡API Calls
💡Cloud 3.5 Sonet
Highlights
Anthropic introduces prompt caching with CLA to reduce costs by up to 90% and latency by up to 85%.
Google's Gemini models were the first to introduce context caching, with similarities and differences to Anthropic's approach.
Prompt caching allows caching frequently used contexts between API calls, reducing expenses for long documents.
Customers can provide more background information with prompt caching, enhancing performance.
Prompt caching is available for Cloud 3.5, Sonet, and Clot 3, with support for Cloud 3.0 coming soon.
Use cases for prompt caching include conversational agents, coding assistants, large document processing, and tool usage.
Cost and latency reduction varies by application, with significant improvements in document chat and multi-turn conversations.
Cached tokens have a 90% reduction in cost, but writing to the cache incurs a 25% overhead.
Gemini models have a different approach, with no cost for cache tokens but a storage cost of $1 per million tokens per hour.
Prompt caching is still in beta, with potential API changes over time.
Key differences between Anthropic and Gemini include token caching limits and cache lifetime.
Best practices for effective caching include caching stable content and placing cache content at the beginning of prompts.
Practical examples demonstrate the implementation of prompt caching in API calls and its impact on latency.
Long context models with prompt caching make it viable to process entire documents without chunking.
Prompt caching is not a replacement for RAG (Retrieval-Augmented Generation) but can complement it.
Enterprise settings with knowledge bases in millions of tokens cannot rely solely on prompt caching due to its limitations.
Long context caching can supercharge RAG by allowing whole documents to be used in model contexts.
The video concludes with a discussion on the practical applications and limitations of prompt caching in comparison to RAG.