📝Prompts

A text input or instruction given to an AI model to guide its response. It serves as the context and directive for the AI, shaping the output by providing specific information, questions or tasks.

1)Chat Prompt Template

The Chat Prompt Template is used to structure prompts using system and human messages for chat-based models.

Key Features:

• Role-Based Prompting: Supports system and human messages • Structured Input: Separates instructions and user input • Dynamic Values: Allows variable placeholders • LangChain Hub Support: Import predefined templates

Setup Requirements:

1. Add Chat Prompt Template node to the canvas

2. Enter System Message (instructions for model)

3. Enter Human Message (user input format)

4. Configure Format Prompt Values if required

5. Connect to chat model

Use Cases:

• Chatbot development • Role-based AI conversations

2)Few Shot Prompt Template

Prompt template you can build with examples.

Key Features:

• Example-Based Learning: Uses input-output examples • Better Accuracy: Improves response quality • Flexible Formatting: Supports prefix and suffix • Custom Separators: Controls example formatting

Setup Requirements:

1. Add Few Shot Prompt Template node to the canvas

2. Enter Example Prompt

3. Add Examples (input-output pairs)

4. Define Prefix and Suffix

5. Set Example Separator

6. Select Template Format

7. Connect to model

Use Cases:

• Training-like prompting • Improving response consistency

3)Prompt Template

Schema to represent a basic prompt for an LLM.

Key Features:

• Dynamic Variables: Supports placeholders like {input} • Simple Prompting: Easy to configure • Reusable Templates: Can be reused across workflows • LangChain Hub Support: Import templates

Setup Requirements:

1. Add Prompt Template node to the canvas

2. Enter Template (with variables)

3. Configure Format Prompt Values

4. Connect to model

Use Cases:

• Basic prompt generation • Reusable AI workflows