What is Prompt Caching?

Large language models (LLMs) are incredibly powerful, but they come with two major challenges cost and latency. Every token processed incurs a charge, and when users repeatedly query the same context, like a large document, they drive up costs by triggering redundant computations.

Also, the latency associated with processing these requests can degrade the responsiveness of your application, leading to a subpar user experience^[1]. Prompt caching emerges as a key solution to address these challenges.

In this article, we will explore what exactly is prompt caching , how it is different from conventional caching, how it can be applied in AI applications, various use cases. We will also look at the benefits and caveats to consider with prompt caching. 

Prompt caching is a straightforward method to improve the speed and cost-efficiency of LLMs. It accomplishes these improvements by storing frequently unchanged parts of a prompt such as instructional content or reference material considerations so the model doesn’t have to reprocess those tokens repeatedly. 

For example, when you send a request to an LLM (such as “explain what is artificial intelligence?”), the model reads the entire prompt to understand and respond. If you send the same prompt again, it repeats the same processing increasing time and cost.

With prompt caching, the model saves repeated parts of a prompt after the first request. When you resend the same prompt, it retrieves the stored response instead of reprocessing it. This process makes responses faster, more efficient and cost-effective, because the model doesn’t have to redo the same computation for identical prompts.^[2]

Goal and scope:

• Prompt caching saves repeated LLM prompts to avoid recomputation and reduce latency.
• Conventional caching stores frequently accessed data or resources (for example, HTML pages, database queries, API responses, images) to make future access faster.

Output reliability:

• Prompt caching works only when the prompt and all parameters (like temperature, top-p and others) remain exactly the same for every request.
• Conventional caching is deterministic, meaning that the same input always produces the same output (like identical query results or images).

Performance:

• Prompt caching reduces token usage, API cost and end-to-end latency for repeated or similar LLM requests.
• Conventional caching improves application performance, lowers backend or database load and decreases network latency.

Expiration and invalidation:

• Prompt caching is often linked to model version updates or retraining cycles.
• Conventional caching manages by using TTL (time-to-live), versioning or manual invalidation when underlying data changes.^[3]

In prompt caching, the repeated sections of a prompt are stored, so that they can be reused in future requests, avoiding the need to resend and reprocess them.

Here’s the step by step guide to how it operates: 

1. Key generation: When a prompt is submitted, the system creates a cache key. 
   Exact‑match caching uses the prompt text (or a hashed or normalized form) and can include parameters such as model name, temperature or system prompt. It works only when the prompt and all settings are identical.
   Semantic caching, alternatively, generates an embedding of the prompt and searches for semantically similar entries. This type of caching allows reuse even if the wording changes. These two caching methods are separate approaches with different strengths.
2. Cache lookup: The key or embedding is then checked against the cache store typically a key-value store for a more traditional cache, or a vector database for semantic matches.
3. Cache hit and miss:  A cache hit returns a stored result immediately, avoiding reprocessing. A cache mis-sends the prompt to the model, then it saves the output for next time.
4. Store the result: While there is a cache miss, once the LLM returns a response the output generated (and sometimes embedding or other metadata) will be saved under that key for use in the future.
5. Invalidation: Cache entries can expire based on server-defined time-to-live (TTL), model updating or content drift to keep answers accurate and timely.

LangChain provides a flexible framework for adding prompt caching into LLM applications. In contrast to stand-alone caching systems, LangChain is an integrated framework for building LLM applications. It brings caching together with other necessary components into one solution such as chaining, memory, retrieval-augmented generation (RAG) and tool integration.

To learn more about how to implement prompt caching by using lang chain, click the following link.

• Interactive documentation portals: Prompt caching is highly effective in internal knowledge management systems by storing responses to frequently asked queries about organizational policies, procedures or technical documentation. When employees repeatedly search for similar information, cached responses provide instant answers without repeatedly querying the LLM. 
  
  
• Marketing campaigns: Prompt caching streamlines chatbot and automation systems by precomputing and saving answers to commonly asked questions. For instance, queries about discounts, product specifications or stock availability, are streamed to ensure that users are provided with quick responses to commonly asked questions. During times of high user traffic, cached responses help avoid unnecessary LLM calls, through reducing unnecessary API costs and easing latency.
  
  
• Customer support systems: On support channels that allow customers to submit questions, answers to the common inquiries for troubleshooting, account management concerns or billing concerns can be cached prompts. It would lead to faster responses, consistency with responses provided and reduced dependency on the LLM processing for the same types of inquiries. These improvements can lead to efficiency gains and improved customer satisfaction.^[4]

• Cost reduction: Save on API costs by reusing prompt-response pairs instead of calling the LLM multiple times.
• Speed: Delivering a cached response provides an immediate answer. It saves the total processing time for the query and increases the response time.
• Intelligent resource usage: Do not waste compute unnecessarily, and reserve resources for executing other, varying, complex queries.
• Infinite capacity: Reasonably manage high traffic and repeat queries and keep the application moving smoothly as it scales.
• Consistent responses: All identical or similar queries produce the same response, time after time, through a cue, building trustworthy and reliable experiences for the user.
• User experience: Being able to provide faster, predictable responses yields more fluid engagements for happier users in a customer-facing application.
• Reduced server workload: Reduce server workload and allow your system to operate with more efficiency by offloading queries that can be answered with a cached response. ^[5]

Prominent platforms use prompt caching stores through cache writes and retention management, while using ttl and cache control to manage their outputs and respect rates, ensuring data privacy and use pricing or costs in relation to the cache. Embedding schemas and system instructions can be cached for use with real-time interactions to improve tool useability across multiple multiturn conversations. 

Though prompt caching can have worthwhile benefits, it is important to remain mindful of limitations as it relates to optimization and performance. Some key considerations include: 

• Management of dynamic queries or queries that are context-sensitive: While prompt caching often has limited utility for queries that are dynamic (in time or from prior user input) or queries that are context-sensitive, such as queries that might incorporate real-time updates of data or a repeated multiturn conversation, those queries must produce a response that is responsive to new information.
  
• Stale responses: Cached responses can become stale fairly quickly if data or context has changed, which would provide incorrect or irrelevant information in response to a query.
  
• Complexity for edge cases: Modifying cache to support partial prompt reuse, or for specific mitigations, might cause more issues since the modifications increase complexity to the system design and implementation effort.
  
• Challenges in cache maintenance: Effective management of the cache (for example, expiration settings or storage constraints) can come with operational overhead.^[6][7]

Prompt caching is an intelligent caching strategy that enhances the performance of AI-driven systems such as chatbots, coding assistants and RAG pipelines to improve both performance and efficiency. Instead of making multiple requests to the API with same request for the full prompt or system prompt, the system takes advantage of cached content (for example, prompt prefixes, cache prefixes, static content) to represent that data saving on both input tokens and output tokens.

The result is a reduction in the number of API calls, reduced cache misses and an overall significant increase in response latency and user experience. 

Prompt caching works best for intrinsic to AI-powered systems like chatbots, enhances performance by leveraging user messages, reducing API requests and dramatically increasing cache hit rates through concise cache read.

For running a function, or responding to subsequent requests or queries in your application; prompt caching saves prompt tokens, total tokens and token counts, plus improves the ease of tracking metrics. With prompt caching, teams can track prompt tokens and total tokens in real time and continue to scale large language models at well-timed cost, speed and reliability across a global scale for generative AI use cases.