Prompt Templates That Work for Every Use Case

What Makes a Good Template

A prompt template has fixed structural elements (the parts that stay the same across uses) and variable slots (the parts you fill in for each specific application). The fixed elements encode prompt engineering best practices: role definition, task specification, output format, constraints, and example placement. The variable slots accept the domain-specific content: the actual role, the specific task, the required format, the relevant constraints, and the representative examples.

The value of a template is not the specific words (those change with every use case) but the structure and the prompting patterns it encodes. A good classification template includes slots for the categories, the classification criteria, the output format, and examples, because these are the elements that every classification prompt needs. By using the template, you ensure you do not forget to include boundary case examples or specify how to handle ambiguous inputs, even if you are in a hurry.

Templates also enable prompt versioning and systematic testing. When your prompt is a template with clearly defined variable slots, you can test different fills independently: try different examples, adjust the constraints, modify the role, each as a controlled change that you evaluate against your evaluation dataset. This is much harder with one-off prompts that blend structure and content into an undifferentiated block of text.

The Universal Template Structure

Almost every effective prompt, regardless of task type, follows this structure. The specifics of each section vary by use case, but the sections themselves are consistent.

Section 1: Role and context. Who is the model? What is its expertise? What is the application context? "You are a [specific role] working in [specific context]." This sets the model's knowledge domain and communication style.

Section 2: Task description. What exactly should the model do? What input will it receive? What output should it produce? "Your task is to [specific action] given [specific input type]. You should [specific instruction for how to approach the task]." Be specific enough that two different people reading this section would produce similar outputs.

Section 3: Output format. How should the response be structured? What fields, sections, or formatting does it need? "Respond in [specific format]. Include [required elements]. Do not include [prohibited elements]." Format specifications prevent the model from choosing its own format, which varies between runs.

Section 4: Constraints and rules. What must the model avoid? What boundaries apply? "Do not [prohibited behavior]. If [edge case], then [required handling]. Always [required behavior]." Constraints are the guardrails that prevent the model from doing things your application cannot tolerate.

Section 5: Examples (when using few-shot). Two to five examples demonstrating the expected input-output pattern. Each example should show correct behavior, and at least one should demonstrate an edge case or boundary decision.

Section 6: The actual input. The user's message or the data to process, clearly delimited from the instructions above.

Classification Template

Classification is the most common prompt task: assigning an input to one of several predefined categories. The template structure is: role (optional), category definitions, classification criteria, output format, examples (at least one per category), and the input to classify.

The critical element most classification prompts miss is the classification criteria: what distinguishes one category from another, especially at the boundaries. "Classify this support ticket as bug, feature request, or question" is ambiguous. "A bug is a report of something that does not work as documented. A feature request is a suggestion for new functionality that does not exist. A question is a request for information about existing functionality" is clear. Including the criteria, not just the category names, dramatically improves classification accuracy.

For multi-class classification with many categories (10 or more), include a "confidence" field in the output that forces the model to express how certain it is about its classification. Low-confidence classifications can be routed to human review, creating a practical system that handles the easy cases automatically and escalates the hard ones.

Extraction Template

Extraction tasks pull structured data from unstructured text: entities from documents, fields from emails, attributes from product descriptions. The template structure is: role, the schema to extract (with field descriptions and types), handling rules for missing or ambiguous data, output format (always structured output), examples showing the extraction from sample text, and the text to extract from.

The handling rules for missing data are the most important part. Without them, models hallucinate plausible values for fields they cannot find in the text, producing data that looks correct but is fabricated. Explicit instructions like "If the delivery date is not mentioned in the text, set delivery_date to null. Do not estimate or infer dates that are not explicitly stated" prevent this failure mode.

Analysis Template

Analysis tasks require the model to examine data or text, identify patterns or issues, and produce an assessment. The template structure is: role (with relevant analytical expertise), what to analyze and what to look for, the analytical framework to apply (if specific), output structure (sections, findings format, recommendation format), constraints (do not speculate beyond the data, cite specific evidence), and the material to analyze.

Analysis templates benefit strongly from chain-of-thought. Include an instruction to "work through your analysis step by step before presenting your findings" or structure the output to include a "reasoning" section before the "conclusions" section. This produces more thorough, evidence-based analysis because the model explicitly considers the evidence before reaching conclusions.

Generation Template

Generation tasks produce new content: writing copy, drafting emails, creating descriptions, generating code. The template structure is: role (with specific writing expertise), the content to generate (topic, purpose, audience), tone and style requirements, length requirements, content that must be included, content that must be excluded, and any reference material to draw from.

Generation templates need the most constraints because the model has the most latitude. Without tone constraints, it defaults to generic assistant prose. Without length constraints, it writes either too much or too little. Without content exclusions, it includes disclaimers, hedging, and filler that dilute the quality. The constraint section in a generation template is often longer than the task description, and that is appropriate.

Conversation Template

Conversation templates define a system prompt for multi-turn chat applications. The template structure is: identity and personality, scope of what the assistant can help with, conversation style (formal, casual, technical), information access (what it knows, what it should look up, what it should not answer), escalation rules (when to hand off to a human), and safety rules.

Conversation templates need to handle the statefulness that other templates do not. Instructions for how to use conversation history, when to reference previous turns, and how to handle topic changes are unique to conversational templates. The topic switching and conversation history management pages cover these patterns in depth.

Template Management in Production

In a production system, templates should be stored as version-controlled files, not hardcoded in application logic. Each template is a text file with clearly marked variable slots (using a consistent syntax like {{variable_name}} or {variable_name}). The application loads the template, fills in the variables, and sends the resulting prompt to the model.

This separation of template from application code enables several important workflows. Prompt engineers can modify templates without touching application code. Templates can be A/B tested by serving different versions to different users. Template changes can be reviewed in pull requests, just like code changes. And the testing pipeline can run automatically on template changes, catching regressions before deployment.

For teams with multiple prompt engineers, establish a template library: a shared repository of tested, documented templates for common tasks. New applications start from a relevant template and customize from there, rather than starting from scratch. This avoids the problem of each developer writing structurally different prompts that have inconsistent quality and are hard to maintain.