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Prompt Templating

Steps

  1. Overview
  2. Pre-requisites
  3. Key Choices and Guidelines
  4. Implementation

1 Overview​

Description​

Prompt templating is a method used to create structured and reusable prompts for Large Language Models (LLMs). It involves converting static prompts into dynamic templates by using placeholders or variables that can be replaced with actual values at runtime. This approach allows developers to program, store, and reuse prompts efficiently across different tasks and applications.

When using AI core this is achieved using the prompt registry. It is also possible to store and manage prompts as strings in code, however it is not recommended.

Expected Outcome​

Create reusable templates for specific tasks to ensure performance, reliability and efficiency of LLM responses.

Benefits​

  • Consistency and Reusability: Templates ensure that prompts are structured consistently, which helps in achieving reliable responses from LLMs. They can be reused across different models and tasks.
  • Efficiency: enabling quick adaptation and reuse of structured instructions across different tasks and contexts without the need for manual rewriting.
  • Customization: Templates allow for easy modification based on specific requirements or user inputs.