Prompt Caching: Because Who Has Time for Slow AI?

Cutting costs with AI isn’t science fiction. It’s just caching.

Prompt Caching : First introduced by Anthropic , adopted by OpenAI … and LLama to follow soon

Let’s understand Prompt Caching for LLMs

Prompt caching is (one of the) advanced optimisation technique in the world of LLMs. It helps solve a critical challenge in modern AI systems like reducing latency and costs when processing repetitive or similar prompts. As AI becomes more integrated into real-time applications like customer service chatbots and virtual assistants, optimizing LLM performance is essential. This post will explore the technical aspects of prompt caching, its architecture, real-world applications, and its role in improving the efficiency of LLM-based systems.

WTH is Prompt Caching?

Prompt caching is a technique that improves processing efficiency by storing and reusing frequently used components of prompts. It is particularly useful when similar prompts are repeatedly processed, leading to significant improvements in performance, lower latency, and cost savings.

The Architecture of Prompt Caching

When an LLM processes a prompt, it typically consists of two parts:

1. Static portion: Parts that remain unchanged across multiple requests, such as instructions or context.
2. Dynamic portion: User-specific queries or content that varies with each request.

In traditional LLM workflows, the entire prompt is reprocessed every time, leading to inefficiencies, especially when the static content is repeatedly processed. Prompt caching changes this by identifying and storing the static portion for future use. Here’s how the system works:

Prompt Decomposition: When a prompt is submitted, the system breaks it into static and dynamic parts. This is crucial as it isolates repetitive content from unique, query-specific elements.

Cache Lookup: After identifying the static part, the system checks if this has already been cached. If a match is found (a cache “hit”), the system skips processing the static content and directly reuses it.

Response Reconstruction: The LLM processes only the dynamic portion, combining the pre-cached static part with the new data, leading to faster responses and reduced computation.

Cache Management: Since storage is limited, the system uses algorithms like Least Recently Used (LRU) or Least Frequently Used (LFU) to remove old data and make space for new entries.

Token-Level Caching: Advanced systems can store partial computations of token sequences, allowing the LLM to skip over redundant token predictions. This is especially useful for scenarios like long document parsing where sections are often repeated.

How Prompt Caching Works in Practice

Prompt caching is typically applied to longer prompts (for ex OpenAI says 1024 tokens and above) and follows this process:

1. Cache Lookup: When a new prompt is submitted, the system checks if a matching static portion exists in the cache.
2. Cache Hit: If a match is found, the cached response is returned almost instantly, significantly reducing latency.
3. Cache Miss: If no match exists, the prompt is processed in full, and the static portion is added to the cache for future use.

Cached prompts usually remain available for 5 to 10 minutes during inactivity, but they can last up to an hour during off-peak times (specific toOpenAI).

Real-World Example

Imagine an application where users can “chat with a book.” The initial prompt might include a long excerpt from a book (e.g., 100,000 tokens) along with instructions on how to engage with the text.

• Without Prompt Caching: Every time a user asks a question, the model has to process the entire 100,000-token prompt again. This could take approximately 11.5 seconds to generate a response.
• With Prompt Caching: By caching the static portion of the prompt after the first interaction, subsequent queries can be processed much faster, potentially reducing the response time to 2.4 seconds per question. This would lead to a latency reduction of around 79% and operational cost savings of up to 90%.

Key Technical Benefits of Prompt Caching

1. Reduced Latency: By skipping the reprocessing of static content, prompt caching drastically reduces the time taken to generate responses. This is particularly important in high-traffic environments like customer support, where quick response times are critical.
2. Computational Efficiency: Reprocessing static parts of prompts is wasteful. By caching these components, the system reduces the overall number of tokens processed per request, freeing up resources for other operations. This can lead to significant cost savings, especially in environments where API calls are expensive.
3. Consistent Responses: Every time an LLM processes a prompt from scratch, slight variations in responses can occur due to the probabilistic nature of the model. Caching the static parts ensures that identical prompts produce consistent outputs, which is crucial for tasks like customer service.
4. Scalability: As demand on LLM-powered applications grows, prompt caching becomes essential for scalability. Systems handling millions of queries daily can maintain high performance without needing proportional increases in computational power, ensuring they can scale without degrading user experience.

Use Cases for Prompt Caching

1. Chatbots and Virtual Assistants: Users often ask repetitive questions or seek predefined responses. Prompt caching allows these systems to deliver quicker responses by fetching previously cached replies, improving user experience.
2. Content Generation Systems: For AI systems that rely on templates to generate content, caching the template ensures only user-specific data is processed, significantly speeding up the process.
3. Interactive Learning Systems: In educational platforms, prompt caching can be used to store static lesson content. Only student-specific queries need to be processed, leading to quicker interactions.

Pricing Benefits of Prompt Caching

OpenAI has structured its pricing model (as of 1st Oct 2024) to reflect savings from using prompt caching. Below is a summary of pricing before and after applying caching discounts:

OpenAI Prompt Caching pricing as of 1st October 2024

This pricing structure illustrates how developers can achieve substantial savings while enhancing application efficiency through cached prompts.

Challenges and Limitations of Prompt Caching

While prompt caching offers significant benefits, it comes with a few challenges:

• Cache Invalidation: Determining when to invalidate cached data is crucial. If the static content changes (e.g., due to updated instructions), the cache must be refreshed to prevent outdated responses.
• Memory Overhead: Caching can lead to significant memory usage, especially in large-scale applications where many prompts are stored. Systems must balance cache size with performance to avoid excessive memory consumption.
• Cache Miss Penalty: If the system encounters a cache miss, the entire prompt must be processed from scratch. This can introduce delays, particularly in environments that rely heavily on prompt caching.
• Dynamic Content Overload: In some cases, the dynamic part of a prompt can be so substantial that caching the static portion offers minimal benefit. Highly personalized interactions, for example, may limit the effectiveness of prompt caching.

Conclusion: The Future of Prompt Caching in LLMs

As AI systems evolve, prompt caching will play an increasingly pivotal role in enhancing the performance and scalability of LLMs. By leveraging prompt caching, developers can build more efficient, responsive, and cost-effective AI applications. This technique will be essential for businesses looking to reduce latency, cut costs, and offer seamless AI-powered user experiences.

Looking ahead, we may see innovations in adaptive caching, where machine learning algorithms predict which prompts are most likely to benefit from caching. Furthermore, distributed caching architectures could enable sharing of cached data across systems, leading to even greater efficiencies in large-scale deployments.