Principle:Microsoft Semantic kernel Prompt Invocation
| Knowledge Sources | |
|---|---|
| Domains | AI_Orchestration, Natural_Language_Processing |
| Last Updated | 2026-02-11 19:00 GMT |
Overview
Prompt Invocation is the principle of sending natural language prompts to AI services through an abstraction layer that decouples the caller from the specific provider, model, and transport mechanism.
Description
At the heart of any AI orchestration framework is the ability to send a prompt to a language model and receive a response. Prompt Invocation formalizes this interaction through an abstraction layer that sits between the application code and the underlying AI provider. Rather than calling an AI provider's SDK directly, the developer submits a prompt string to the kernel, which resolves the appropriate AI service, formats the request, sends it to the provider, and returns a structured result.
This abstraction provides several important benefits. First, it enables provider independence: the same prompt invocation code works regardless of whether the kernel is configured with OpenAI, Azure OpenAI, or any other supported provider. Second, it provides a uniform result type (FunctionResult) that encapsulates the AI response along with metadata such as token usage, model information, and function call results. Third, the kernel can apply cross-cutting concerns during invocation, including logging, telemetry, prompt rendering, and filter execution, without the application code needing to be aware of these operations.
The invocation model treats each prompt as a kernel function -- specifically, a dynamically created function that wraps the prompt string. This unification means that prompt invocation and plugin function invocation share the same execution pipeline, including the same filter chain, event model, and result type. This consistency simplifies the programming model and enables powerful composition patterns where prompts and functions can be chained together.
Usage
Use prompt invocation for any scenario where you need to send a natural language prompt to an AI model and receive a complete response. This is the most basic form of AI interaction in Semantic Kernel and is suitable for simple question-answering, content generation, summarization, and classification tasks. For scenarios requiring real-time feedback, consider streaming invocation instead.
Theoretical Basis
Prompt Invocation implements the Command Pattern where each prompt is encapsulated as a command object (a KernelFunction) that is executed by the kernel. The execution follows a well-defined pipeline:
Pipeline:
1. Prompt String → KernelFunction (wrapping)
2. KernelFunction → Rendered Prompt (template rendering)
3. Rendered Prompt → AI Service Request (service resolution + formatting)
4. AI Service Request → AI Service Response (network call)
5. AI Service Response → FunctionResult (result extraction)
Formal invocation model:
InvokePromptAsync(prompt, args) =
let fn = CreateFunctionFromPrompt(prompt)
let rendered = fn.RenderPrompt(args)
let service = kernel.Resolve(IChatCompletionService)
let response = await service.GetChatMessageContentsAsync(rendered)
return FunctionResult(response)
The invocation is asynchronous by design, reflecting the inherent latency of AI service calls. The Task<FunctionResult> return type allows callers to use the standard async/await pattern and to compose multiple invocations concurrently when appropriate.
The FunctionResult type serves as a monad-like wrapper that encapsulates either a successful AI response or error information, along with metadata. It provides typed access to the response content through GetValue<T>() and implicit string conversion for common use cases.