Creating prompts

When you need to specify and verify the analysis or output of contextual content, you can create prompts to generate outputs by using large language models (LLM) in the prompt editor. You can also provide inputs and expected outputs as examples to improve the performance of the generation with few-shot prompting. You can use the generative AI model in your decision model or task model.

1. From the Model pull-down menu, select a model.
2. Create a prompt. See the following steps.
   Tip: A default prompt is provided. You need to modify it if you want to create a new prompt.
3. Optional: Enter an imperative statement in the Context pane.
4. Optional: Enter the text that you want the model to answer to in the Prompt input pane.
5. Optional: Add variables.
   Variables are used as inputs in generative AI components. At least one variable must be defined even if it is not used.

   Remember: A default variable is provided and it cannot be deleted even if it is not used.

   1. Click New variable in the Variables pane.
   2. Name the variable, and enter its default value. The name and value of variables must be string.
   3. Insert the variable in your prompt in the Prompt input or Context pane.

      The variable name must be surrounded by double curly brackets, for example: {{topic}}.

      You can automatically insert variables by clicking the Add variable icon in the pane and select a variable from the list, or pressing Ctrl+Space in the pane and select a variable.

6. Optional: Set tokens in the Parameters pane to constrain the length of generated outputs.
   Tip: A token is a collection of characters that has semantic meaning for a model. The words in your prompt text are converted into tokens before they are processed by LLM. The raw result from a model is also tokens. The output tokens are converted back into words to be displayed as the result.

   • The minimum and maximum values are set to 1 and 50 by default.
   • The minimum value cannot be 0.
   • The limit of the maximum value varies depending on the model that you selected.
7. Click Generate.

   You can also click the Raw prompt icon in the pane to see the raw prompts. In View raw prompt, you can see the context, the prompt input, and training examples that are used to obtain the generated output.

8. Adjust your prompt to get better results if necessary.
9. Optional: You can also add training examples.

   You can add examples to the prompt to improve the precision, quality, and stability of the output generated with your prompt.

   Specify one or more pairs of sample input and corresponding output.

   Tip: In general, the more input/output pairs you provide, the better your results are. However, if you have too many examples, they might take the token space in the maximum input token that is allowed by the model as well as the overall maximum token that is allowed for both input and generated tokens.
   1. In the Training example pane, click New example.
   2. Enter input and expected output.
   3. Click Generate to test your prompt. Check if the generated output is improved.