Your team writes the same AI prompts over and over. "Summarize this document for the customer." "Draft a response to this inquiry." "Generate a report on this data." Every time, someone reinvents the wheel.
Worse, every person writes it slightly differently. One version works well. Another produces garbage. Nobody knows which prompts are tested and which are experiments.
Now multiply this by 50 team members running 200 prompts a week. Inconsistent outputs. Wasted time. No way to improve systematically because nothing is captured.
Prompt templating turns ad-hoc copy-paste into a structured library of reusable prompts with variables that get filled in at runtime.
INTELLIGENCE INFRASTRUCTURE - The reusable building blocks that make AI behavior consistent and maintainable across your organization.
A prompt template is a reusable AI instruction with placeholders for variables. Instead of writing 'Summarize this customer feedback email from Sarah Chen about her order #1234' every time, you create a template: 'Summarize this {{content_type}} from {{person_name}} about {{topic}}'. The template stays constant. The variables change per use.
The power isn't just in saving keystrokes. It's in capturing what works. When you find a prompt structure that produces great results, you template it. Now everyone uses the proven version. You can improve it once, and improvements propagate everywhere it's used.
Without templating, every AI interaction is a fresh experiment. With templating, you build on what works. You stop reinventing prompts and start refining them.
Prompt templating solves a universal problem: how do you create reusable instructions that can adapt to different contexts without rewriting them each time?
Separate the stable instruction from the variable context. Define placeholders for what changes. Inject the right values at runtime. The template becomes a reusable asset you can test, version, and improve.
Choose a template, fill in the variables, and watch the final prompt assemble.
Summarize the following {{document_type}} for {{audience}}. Focus on: {{focus_areas}} Tone: {{tone}} Document content: {{document_content}}
Summarize the following quarterly report for the leadership team. Focus on: key metrics and action items Tone: professional but concise Document content: [Paste document text here]
The {{variable}} pattern
Define placeholders like {{customer_name}} or {{document_type}} in your prompt text. Before sending to the AI, replace each placeholder with the actual value. 'Summarize this {{document_type}} for {{audience}}' becomes 'Summarize this quarterly report for the leadership team.'
If-then sections
Templates can include or exclude sections based on conditions. If the customer tier is 'premium', include detailed analysis. If the request type is 'urgent', add priority handling instructions. The template adapts its structure based on context.
Lego-style assembly
Break templates into smaller, reusable blocks. A header block sets the role. An instruction block defines the task. A format block specifies output structure. Assemble different combinations for different use cases. Same blocks, different compositions.
Your team responds to 200+ customer inquiries daily. Each response needs to be personalized but consistent. Templates define the structure, variables inject the customer's name, issue, history, and context. Every response follows proven patterns while feeling personal.
Hover over any component to see what it does and why it's neededTap any component to see what it does and why it's needed
Animated lines show direct connections · Hover for detailsTap for details · Click to learn more
You wrote 'Always refer to the customer as valued' directly in your template. Later, you need a formal version for legal documents and a casual version for support chats. Now you have two nearly identical templates to maintain. Changes to one don't propagate to the other.
Instead: Make tone, formality, and style into variables. One template, multiple styles via different variable sets.
Your template expects {{order_number}} but someone passes an empty string. The AI receives 'Look up order number: ' and hallucinates a response. Or worse, it receives 'Look up order number: DROP TABLE orders' from a malicious input.
Instead: Validate all variables before substitution. Type check, sanitize, and provide defaults for missing values. Never trust raw input.
You have 50 templates scattered across team members' documents, Notion pages, and code comments. Nobody knows which version is current. Someone 'improved' a template that was actually working fine. There's no history, no testing, no ownership.
Instead: Centralize templates in one system. Version them. Test them before promoting to production. Assign owners. Treat them like code.
You've learned how to create reusable prompt structures with variables. The natural next step is organizing these templates into a layered system prompt architecture.