How Prompt Caching Can Cut Redundant Token Costs in LLM Chatbot Requests

Developers building chatbots with large language models often re-send the same lengthy system prompts, documents, and conversation history on every request, paying repeatedly for identical tokens. Prompt caching, using a cache_control breakpoint, allows the model to reuse previously processed content instead of recomputing it each turn. Cache entries default to a 5-minute TTL, which refreshes automatically on each hit, though a 1-hour TTL option suits workflows where follow-up requests may arrive after longer delays. A lookup window of only 20 blocks means fast-growing conversations can miss the cache entirely, so placing a second breakpoint further back helps maintain reliable hits. Developers can also pre-warm the cache before real traffic arrives by sending a request with max_tokens set to 0, generating no output but writing the cache entry at zero token cost.