A well-designed prompt template has: a system prompt section (constant — defines behavior, rules, and output format), a context section (variable — filled with retrieved documents, user history, or other data), and a user input section (variable — the actual user request). The system prompt stays the same across all requests. The context changes based on what's relevant. The user input changes every time.
In production, prompt templates need: version control (track changes, rollback if quality drops), A/B testing (compare template variations), variable validation (ensure required fields are filled, inputs are within limits), and output parsing (extract structured data from model responses). Frameworks like LangChain, Promptfoo, and Braintrust provide tooling for template management, but even a simple system of template files + version control goes a long way.
Common mistakes: overly complex templates that try to handle every case (better to have multiple focused templates), templates that include unnecessary context (wasting tokens and confusing the model), hard-coding information that changes (use variables), and not testing templates against edge cases (unusual inputs, adversarial inputs, empty fields). The best templates are as simple as possible while reliably producing the output you need.