1 Dynamic Prompting and Kernel Arguments in Semantic Kernel
Semantic Kernel enables dynamic, context-aware AI interactions by combining flexible prompt templates with configurable execution controls. Instead of relying on fixed instructions, developers can compose prompts that adapt to user input and runtime context, and then fine-tune how the model responds. Together, dynamic prompting and Prompt Execution Settings provide both the expressive power to shape content and the governance to shape behavior, resulting in more reliable, precise, and intelligent applications.
Dynamic prompts replace static text with variables and even run functions during prompt construction. Kernel Arguments power this adaptability by interpolating runtime values into placeholders and by invoking functions as part of the template, a process known as rendering. Illustrated with a robot-car scenario, developers can inject a list of basic movements, pass user instructions, and have the assistant decompose complex commands into a sequence of simple actions—showing how variable interpolation and function execution turn prompt templates into responsive, context-sensitive instructions.
Prompt Execution Settings complement Kernel Arguments by controlling model behavior at inference time. Using options such as temperature, maximum tokens, system prompts, user identifiers, and token-level probabilities (via Logprobs), developers can steer creativity, constrain length, and improve observability. The examples demonstrate setting OpenAI-specific execution parameters, producing concise outputs (like a single movement name), and inspecting token probabilities for debugging and prompt tuning—closing the loop between dynamic prompt design and measurable, controllable model performance.
Summary
Kernel Arguments enable dynamic prompts by allowing variable interpolation and function execution within prompt templates.
Dynamic prompts adapt to runtime context, making AI interactions more flexible and relevant.
Prompt Execution Settings let you control model behavior (e.g., randomness, response length, user tracking, and token probabilities).
Rendering is the process of producing the final prompt by replacing placeholders and executing functions.
Inspecting token probabilities helps you understand and debug the model’s decision-making process.
By leveraging dynamic prompting and Kernel Arguments, you can build smarter, more adaptive AI applications with Semantic Kernel.
FAQ
What is the difference between static and dynamic prompts in Semantic Kernel?Static prompts are fixed templates that do not change at runtime. Dynamic prompts adapt to runtime inputs and context, using Kernel Arguments to inject values or run functions, enabling more flexible, context-aware interactions.What are Kernel Arguments and why are they important?Kernel Arguments are name-value pairs passed to a prompt that customize its content and influence model behavior. They enable variable interpolation (filling placeholders with values) and function execution inside prompts, making prompts dynamic and reusable.How does variable interpolation work in a prompt template?You place placeholders like {{$name}} in the template and provide corresponding values via Kernel Arguments at runtime. During rendering, Semantic Kernel replaces each placeholder with its provided value before sending the prompt to the model.Can I execute functions inside a prompt, and how?Yes. You can call functions using syntax like {{plugin.function $arg}}. At render time, Semantic Kernel executes the function (e.g., engine.move with $distance) and injects the result into the final prompt.What does “rendering” mean in this context?Rendering is the process of producing the final prompt by resolving placeholders (variable interpolation) and executing any embedded functions. The rendered prompt is what the AI model actually receives.How do I pass user input and other context into a dynamic prompt?Provide them as Kernel Arguments keyed to the placeholders in your template (for example, "input" or "movements") and invoke the prompt (e.g., kernel.InvokePromptAsync(promptTemplate, kernelArguments)). The placeholders are replaced during rendering.How can I adjust the AI model’s behavior using Prompt Execution Settings?Use PromptExecutionSettings (or provider-specific variants like OpenAIPromptExecutionSettings) to configure parameters such as Temperature, MaxTokens, ChatSystemPrompt, User, and Logprobs. These settings guide how the model generates responses.What do Temperature, MaxTokens, ChatSystemPrompt, User, and Logprobs control?- Temperature: randomness/creativity of outputs (higher is more creative). - MaxTokens: maximum length of the response. - ChatSystemPrompt: the system-level instruction shaping assistant behavior. - User: an identifier for the end-user (useful for tracking/abuse detection). - Logprobs: requests token-level probabilities for analysis.How do I inspect token probabilities from a response?Enable Logprobs (and optionally TopLogprobs) in Prompt Execution Settings. After invocation, read token probability details from response.Metadata (e.g., ContentTokenLogProbabilities) to see top tokens and their log probabilities.When should I prefer dynamic prompts over static prompts?Use dynamic prompts when responses must adapt to user input, context, or runtime data, or when you need to compose prompts from variable content or function outputs. Static prompts are fine for fixed, repeatable interactions that don’t require such flexibility.
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