Principle:Microsoft Semantic kernel Prompt Templating
| Knowledge Sources | |
|---|---|
| Domains | AI_Orchestration, Template_Rendering |
| Last Updated | 2026-02-11 19:00 GMT |
Overview
Prompt Templating is the principle of parameterizing prompt strings with named variables that are resolved at invocation time, enabling dynamic and reusable prompt construction.
Description
Static prompts are inherently limited: they always produce the same request to the AI model, regardless of the context. Prompt Templating solves this by introducing variable placeholders into prompt strings that are substituted with actual values at runtime. This transforms prompts from static strings into dynamic templates that can adapt to different inputs, user contexts, and application states.
In Semantic Kernel, the default template syntax uses the Template:$variableName pattern, where $variableName refers to a key in the KernelArguments dictionary. When the prompt is invoked, the template engine scans the prompt string for these placeholders, looks up the corresponding values in the arguments, and performs text substitution before sending the rendered prompt to the AI service.
This design follows the well-established template engine pattern used in web frameworks (such as Handlebars, Mustache, and Razor), adapted for the AI prompt context. The key difference is that prompt templates operate in a domain where the rendered output is consumed by a language model rather than a web browser, which introduces unique considerations around prompt injection, token efficiency, and output formatting. Semantic Kernel supports multiple template formats, with the default "semantic-kernel" format being the most commonly used, and Handlebars format available as an alternative for more complex logic.
Usage
Use prompt templating whenever a prompt needs to incorporate dynamic data. Common examples include inserting a user's question into a system-prompt-wrapped template, parameterizing the topic of a content generation request, or injecting context documents into a retrieval-augmented generation (RAG) prompt. Template variables should be preferred over string concatenation because they integrate with the kernel's rendering pipeline and are subject to template-level validation.
Theoretical Basis
Prompt Templating is an application of String Interpolation formalized through a template engine. The core operation is a substitution function:
Render(template, arguments) → rendered_string
Where:
template = "What color is the {{$topic}}?"
arguments = { "topic" → "sea" }
Render(template, arguments) = "What color is the sea?"
Formal definition:
Given a template string T containing placeholders p1, p2, ..., pn and an argument map A: string -> string, the rendering function R is defined as:
R(T, A) = T with each {{$pi}} replaced by A[pi]
Preconditions:
For each pi in T, pi must exist as a key in A
(otherwise: render error or empty substitution, depending on configuration)
Properties:
R is idempotent if output contains no template syntax:
R(R(T, A), A) = R(T, A)
R is compositional:
R("{{$a}} and {{$b}}", {a→x, b→y}) = R("{{$a}}", {a→x}) + " and " + R("{{$b}}", {b→y})
The KernelArguments type serves as the argument map. It extends the basic substitution model by also carrying PromptExecutionSettings, which are not substituted into the template but are passed to the AI service to control generation behavior. This dual role makes KernelArguments both a template context and a service configuration carrier:
KernelArguments = {
templateVariables: Dictionary<string, object>,
executionSettings: Dictionary<string, PromptExecutionSettings>
}