Prompt Caching: What's so good about it?

Can prompt caching replace RAG's

Prompt Caching and Retrieval-Augmented Generation (RAG) are two powerful techniques used in AI to enhance performance and efficiency. However, to make the most out of Prompt Caching, especially under token limits like the 999,999 tokens offered by Anthropic's models, it’s crucial to manage and summarize texts effectively.

What is Prompt Caching?

Prompt Caching involves storing the results of frequently used prompts or queries. When the same or similar prompts are requested again, the cached results can be retrieved quickly without reprocessing, offering several benefits:

Reduced Latency: Faster response times by eliminating repetitive processing.

Improved Efficiency: Lower operational costs due to reduced computational demands.

Enhanced Experience: Provides accurate answers for complex questions over long documents

What is Retrieval-Augmented Generation (RAG)?

RAG combines retrieval mechanisms with generative models. It retrieves relevant pieces of information and then uses a generative model to create contextually accurate responses.

This area of LLM development has seen multiple developments from Graph-RAG , Vector Search to Hybrid approaches.

Based on my experiments with some of these approaches I find them to be very complex for marginal improvements

While powerful, RAG can struggle with very large texts and complex contexts, which is where Prompt Caching shines.

Challenges with RAG in Handling Full Context

While RAG is adept at fetching relevant information, it can struggle with:

Fragmented Context: By breaking texts into smaller pieces, RAG might miss interdependencies or nuanced details.

Incomplete Retrieval: Large documents or datasets can sometimes overwhelm RAG, leading to incomplete context retrieval.

In comparison, Prompt Caching can handle the entire context directly, making it suitable for complex reasoning scenarios.

Why is Prompt Caching better ?

Consider the below example

Complex reasoning involves understanding two different concepts and drawing a conclusion. Traditional retrieval-augmented generation (RAG) tackles this by making multiple queries about the user's question, compiling the results, and then having the language model (LLM) summarize them. This approach works for straightforward cases but often fails when information is spread across multiple documents and topics. Though it can be fixed with more advance RAG techniques, it becomes cumbersome and time consuming.

LLMs excel at this because they inherently contain a vast amount of interconnected information, thanks to their billions of parameters. However, fine-tuning these models requires extensive data generation, which can be resource-intensive.

Prompt caching offers a solution by allowing the LLM to access the entire dataset effectively, leveraging the "black box" nature of modern LLMs to handle complex queries more efficiently.

Putting it all together:

If Prompt Caching is really that good, what’s the catch ?
Hint: Token Limit

Given token limits like the 999,999 tokens of Anthropic's models, the key to getting it to work is strategies to summarize the context to fit the token limit:

Understanding Token Limits, Summarization, and Text Segmenting

It's essential to know your token limit to decide which text sections to cache and when to update. Efficient summarization involves focusing on critical components, using LLMs for automated summaries, and manually refining these summaries. To improve summarization and caching, break down texts into manageable sections and summarize each chunk separately before combining.

Compression, Hierarchical Caching, and Dynamic Updating

Optimize token usage by using abbreviations, eliminating redundant phrases, and retaining key terms. Implement a hierarchical approach to caching with brief summaries for general queries, more detailed summaries for in-depth queries, and comprehensive summaries for the most detailed inquiries. Continuously monitor which sections are accessed frequently and update summaries accordingly to ensure relevance and efficiency.

I will be covering strategies for prompt caching in detail in a follow up blog post .

Conclusion

Prompt Caching and Retrieval-Augmented Generation (RAG) are both formidable techniques that significantly enhance the performance and efficiency of AI models. While RAG is excellent for quick and targeted information retrieval, it can encounter challenges when dealing with large and complex texts. On the other hand, Prompt Caching excels in handling extensive contexts and complex reasoning scenarios By optimizing token usage, employing hierarchical caching, and ensuring dynamic updates, one can maximize the benefits of Prompt Caching to deliver fast, efficient, and contextually accurate responses.