Create a Prompt - Endprompt

This guide walks you through creating a new prompt for your endpoint.

Prerequisites

Before creating a prompt, ensure you have:

An endpoint created
Input schema defined (so you know what data you’ll receive)
Output schema defined (optional, but recommended)

Creating a Prompt

Navigate to the Endpoint

Open the endpoint you want to add a prompt to.

Go to Prompts Tab

Click the Prompts tab in the endpoint workspace.

Click Create Prompt

Click the Create Prompt button.

Configure Basic Settings

Fill in the prompt details:

Field	Description
Name	Descriptive name (e.g., “Detailed Summary v2”)
Model	Select the LLM to use
Temperature	Randomness (0.0 - 1.0)
Max Tokens	Maximum response length

Write the Template

Enter your prompt template using Liquid syntax.

Save

Click Save to create the prompt as a Draft.

Prompt Settings Explained

Model Selection

Choose from available models:

Provider	Models	Best For
OpenAI	GPT-4o, GPT-4, GPT-3.5-turbo	General purpose, coding, analysis
OpenAI	gpt-image-1	Image generation and editing
Anthropic	Claude 3 Opus, Sonnet, Haiku	Long context, nuanced responses
Google	Gemini 2.0 Flash, Gemini 2.5 Pro	Fast multimodal, vision

Start with GPT-4o or Claude 3 Sonnet for the best balance of quality and speed. For image generation, use gpt-image-1.

Select an image generation model when your endpoint has image output fields. Select a vision model (GPT-4o, Claude 3, Gemini) when your endpoint has image inputs with text/JSON outputs.

Temperature

Controls randomness in responses:

Value	Behavior	Use Case
0.0 - 0.3	Deterministic, consistent	Classification, extraction, facts
0.4 - 0.6	Balanced	General tasks, summarization
0.7 - 1.0	Creative, varied	Creative writing, brainstorming

Max Tokens

Maximum tokens in the response:

Short responses (100-500): Classifications, extractions
Medium responses (500-2000): Summaries, analyses
Long responses (2000+): Detailed reports, long-form content

Higher max tokens = higher costs. Set appropriate limits for your use case.

Custom Parameters for Image Generation

When using an image generation model, use the Custom Parameters panel to configure provider-specific options like size, quality, and background. Custom Parameters are defined as a JSON array of name/value pairs:

[
  { "name": "size", "value": "1024x1536" },
  { "name": "quality", "value": "high" },
  { "name": "background", "value": "transparent" }
]

Parameter precedence: Hardcoded defaults → Output Field Example Value → Custom Parameters

These parameters are model/provider-specific. Check your provider’s API documentation for supported values. For example, background is an OpenAI-specific parameter.

Image generation prompts don’t need JSON output instructions — the prompt itself is the image description sent to the generation API.

A system-level instruction that sets context:

You are a professional content editor with 20 years of experience. 
You always provide constructive, actionable feedback.

Not all models support system prompts. When unsupported, it’s prepended to the main prompt.

Writing Effective Templates

Structure Your Prompt

A well-structured prompt typically includes:

Role/Context — Who is the AI in this scenario
Instructions — What to do, step by step
Input Data — The user’s data to process
Output Format — How to structure the response

Example: Complete Prompt

You are an expert content analyst specializing in {{ inputs.industry | default: "general" }} content.

## Your Task
Analyze the provided text and extract key insights.

## Instructions
1. Read the content carefully
2. Identify the {{ inputs.num_insights | default: 3 }} most important insights
3. For each insight, provide:
   - A concise title
   - A brief explanation
   - Relevance score (1-10)

## Content to Analyze
{{ inputs.content | truncate: 10000 }}

{% if inputs.focus_areas %}
## Focus Areas
Pay special attention to:
{% for area in inputs.focus_areas %}
- {{ area }}
{% endfor %}
{% endif %}

## Output Format
Return a JSON object:
{
  "insights": [
    {
      "title": "string",
      "explanation": "string",
      "relevance": number
    }
  ],
  "overall_summary": "string"
}

Return only valid JSON, no additional text.

Common Patterns

Classification

Classify the following text into one of these categories:
{% for cat in inputs.categories %}
- {{ cat }}
{% endfor %}

Text: {{ inputs.text }}

Return JSON: {"category": "selected", "confidence": 0.0-1.0}

Extraction

Extract the following information from the text:
- Names of people
- Dates mentioned
- Key actions or events

Text: {{ inputs.text }}

Return JSON with arrays for each type.

Generation

Write a {{ inputs.tone }} {{ inputs.content_type }} about {{ inputs.topic }}.

Requirements:
- Length: approximately {{ inputs.word_count | default: 200 }} words
- Audience: {{ inputs.audience | default: "general" }}
{% if inputs.keywords %}
- Include these keywords: {{ inputs.keywords | join: ", " }}
{% endif %}

Transformation

Rewrite the following text to be more {{ inputs.style }}.

Original:
{{ inputs.text }}

Maintain the core meaning while improving {{ inputs.focus }}.

Testing Your Prompt

After saving, test immediately:

Click the Test button next to your prompt
Fill in the auto-generated form with sample data
Click Run Test
Review the response
Iterate on your template as needed

Test with edge cases: empty optional fields, very long inputs, unusual values. Your prompt should handle them gracefully.

Best Practices

Be explicit about output format

If you need JSON, say so explicitly and provide an example structure.

Use numbered instructions

LLMs follow numbered steps more reliably than prose paragraphs.

Set clear boundaries

Tell the model what NOT to do: “Do not include explanations outside the JSON.”

Provide examples

Few-shot examples dramatically improve consistency for complex tasks.

Handle edge cases

Use conditionals to handle missing data: {% if inputs.optional_field %}

Next Steps

Testing Prompts

Learn to test prompts thoroughly

Prompt Versions

Understand versioning and promotion

​Prerequisites

​Creating a Prompt

​Prompt Settings Explained

​Model Selection

​Temperature

​Max Tokens

​Custom Parameters for Image Generation

​Writing Effective Templates

​Structure Your Prompt

​Example: Complete Prompt

​Common Patterns

​Classification

​Extraction

​Generation

​Transformation

​Testing Your Prompt

​Best Practices

​Next Steps

Testing Prompts

Prompt Versions

Prerequisites

Creating a Prompt

Prompt Settings Explained

Model Selection

Temperature

Max Tokens

Custom Parameters for Image Generation

Writing Effective Templates

Structure Your Prompt

Example: Complete Prompt

Common Patterns

Classification

Extraction

Generation

Transformation

Testing Your Prompt

Best Practices

Next Steps