Structure the repeatable part
Prompts Chat Index
Give stable instructions and changing conversation context different jobs.
The idea, in context
A prompts chat index organizes the ingredients of a conversational request: reusable instructions, examples, retrieved passages, and the evolving exchange. That organization helps you identify what remains stable without turning a conversation into an undifferentiated block of text. Provider prompt-caching mechanisms have their own matching and retention rules. Your application should keep a versioned prompt record and build requests consistently, then observe whether reuse actually occurs rather than inferring it from similar-looking text.
Make the prompt template explicit
Store the instruction version, expected variables, permitted tools, and output contract together. Review changes like application code. A small edit in the stable portion can matter more to reuse than a long change in the new question.
Place variable context deliberately
Identify timestamps, request identifiers, personalized facts, and retrieved material before assembling the final request. Follow the selected provider’s documented prefix or cache-boundary behavior. Deterministic construction makes misses easier to diagnose and keeps evaluation comparisons meaningful.
Keep chat history governed
Choose which turns the task needs and how old material is summarized or removed. A shorter request can lose important context, while unbounded history can retain material beyond its purpose. Cache eligibility should never substitute for an intentional conversation-retention policy.
Questions about prompts chat index
Will caching return the same answer?
Prompt caching concerns reuse of input processing. It does not by itself mean the application returns a previously saved final answer. Generation behavior still depends on the model and request.
What is a good first experiment?
Choose a repeatable task with a stable instruction block. Compare several equivalent runs using the provider’s usage fields, while holding the model, request shape, and evaluation criteria constant.
Start with one good question.
Follow a topic, explore the tradeoffs, and go deeper in the Lab.