LLM Prompt Component (This is a legacy component, so it is not recommended to use it)

Use the LLM Prompt component to generate text content from a prompt in a single, stateless turn. It's ideal for straightforward tasks like summarization, translation, or content creation where conversation history is not required.

When to use this component

This is a versatile component for single-turn AI generation. For multi-turn, stateful conversations that require memory, consider using the LLM Assistant Component. For the most up-to-date features, see the GenAI LLM Component.

Step 1: Select a Model

Choose the language model that will generate the response.

Field	Description
Model	Select from available models like OpenAI (GPT-3.5, GPT-4) or Echo (which simply mirrors the prompt).
Custom Model	No Connect to your own LLM provider like Amazon Bedrock or Google Vertex AI (Enterprise feature).

Unlocking More Models

You can add credentials for other model providers in the Vault to expand your model selection.

Step 2: Write the Prompt

This is the core instruction for the AI. Craft a clear and specific prompt to guide the model's output. You can include variables from your inputs to make it dynamic.

Example Prompt: Summarize the following text into three key points: {{article_text}}

Step 3: Define Inputs

Inputs are variables you can pass into your prompt from other parts of your workflow.

Field	Required?	Description
Name	Yes	A unique name for the input variable (e.g., article_text).
Type	Yes	The data type (e.g., String, Number, Array, Object).
Description	No	A clear explanation of what the input is for.
Optional	No	Mark as true if the input is not always required.
Default Value	No	A fallback value to use if no input is provided.

Step 4: Configure Advanced Settings

Fine-tune the model's behavior for more control over the generated text.

Temperature: Controls randomness. Lower values (e.g., 0.2) make the output more focused and deterministic. Higher values (e.g., 1.0) increase creativity.

Top P: An alternative to Temperature that controls nucleus sampling. It's recommended to alter one but not both.

Step 5: Define Outputs

By default, the component has one output, Reply, which contains the full response from the model. You can add custom outputs to parse this response and extract specific fields.

Field	Description
Name	A unique name for your custom output (e.g., summary).
Expression	A JSON Path expression to extract data from the Reply. For example, Reply.summary would extract the summary field from a JSON object returned by the model.
Description	An optional description for the output field.

Parsing JSON Responses

To use custom outputs effectively, your prompt should instruct the model to return its response in a specific JSON format. For example: Please return your answer as a JSON object with a "title" and a "summary" field.

Best Practices

• Be Specific in Your Prompt: The most important factor for a good response is a clear, detailed, and unambiguous prompt.
• Structure Your Output: For predictable results, explicitly ask the model to format its response in a certain way (e.g., as JSON, a Markdown list, etc.) and use custom outputs to parse it.
• Tune One Parameter at a Time: When adjusting advanced settings, modify one parameter (like Temperature) and test the result before changing others.
• Use Echo for Debugging: The Echo model is useful for testing how your dynamic inputs are being inserted into your prompt.

Troubleshooting Tips

If your prompt isn't working as expected...

• Empty or Garbage Output: Check if your prompt is clear and if your Max Output Tokens setting is high enough. A low value can cut off the response.
• Fails to Parse JSON: If you are trying to extract fields from a JSON response, run the component in Debug Mode and inspect the raw Reply to ensure the model is actually returning valid JSON in the format you expect.
• Model Refuses to Answer: Your prompt might be hitting a content filter. Try rephrasing the request. Also, check that your API key (if using your own) is valid and has sufficient credits.

What to Try Next

• Chain multiple LLM Prompt components together, where the Reply of one becomes an input for the next, creating a processing pipeline.
• Use a Classifier Component to determine user intent, then route to different LLM Prompt components with specialized prompts.
• Pass the output to a JSON Filter Component to further clean or simplify the data before using it in other steps.