Prompt Template

A prompt template is a pre-written prompt structure with placeholder variables that are filled in with specific data each time it is used. Think of it as a form letter for AI — the structure and instructions remain the same, but the specific details change with each use.

Why use templates?

Without templates, every person on your team writes prompts differently. One person's email summariser produces bullet points, another's produces paragraphs, and a third includes information you did not want. Templates standardise the output, ensuring consistency regardless of who uses them.

Anatomy of a prompt template

A well-designed template includes:

• Role definition: Who the AI should act as (e.g., "You are a senior financial analyst").
• Task description: What the AI should do, clearly and specifically.
• Input placeholder: Where the variable content goes, marked with a clear delimiter like `{{input}}` or `{document_text}`.
• Output format: Exactly how the result should be structured.
• Constraints: Length limits, tone requirements, things to include or avoid.
• Examples: One or two examples showing desired input-output pairs.

Example template

``` Summarise the following customer support ticket.

Ticket: {{ticket_text}}

Provide: - Category (billing/technical/account/other) - Urgency (low/medium/high) - Summary (2-3 sentences) - Suggested response outline (3 bullet points) ```

Template management best practices

• Version control: Track changes to templates just like code. A small wording change can significantly affect output quality.
• Testing: Test templates against diverse inputs before deploying them.
• Documentation: Record what each template does, who created it, and when it was last updated.
• Performance monitoring: Track template effectiveness over time, especially after model updates.

Templates in development

In code, prompt templates are typically managed using string interpolation or template libraries. Frameworks like LangChain provide dedicated PromptTemplate classes that handle variable substitution, validation, and composition.

Templates vs dynamic prompting

Templates work best for repeated tasks with a consistent structure. For exploratory or conversational use cases, dynamic prompting (building prompts on the fly based on context) may be more appropriate. Many production systems combine both.