Prompt Engineering Guide

Best practices for writing effective prompts that create reliable and natural-sounding AI phone agents.

Prompt engineering is the foundation of creating effective AI phone agents. A well-crafted prompt determines how your agent interprets situations, responds to users, and handles edge cases. This guide provides proven strategies for writing prompts that agents can follow reliably.

This guide covers general prompt engineering principles. For agent-specific implementation: Single/Multi Prompt Agents — apply these principles directly in your prompts. Conversation Flow Agents — use these principles within individual node instructions.

To see examples of effective prompts, create a new agent in the dashboard and explore the pre-built templates.

Best Practice 1: Use Sectional Prompts

Break large prompts into focused sections for better organization and LLM comprehension.

Benefits:

• Reusability — sections can be adapted across different agents
• Maintainability — update specific behaviors without affecting others
• Clarity — LLMs process structured information more accurately

Recommended Prompt Structure

## Identity
You are a friendly AI assistant for [Company Name].
Your role is to [specific purpose].
You have expertise in [relevant domains].

## Style Guardrails
Be concise: Keep responses under 2 sentences unless explaining complex topics.
Be conversational: Use natural language, contractions, and acknowledge what the caller says.
Be empathetic: Show understanding for the caller's situation.

## Response Guidelines
Return dates in spoken form: Say "January fifteenth" not "1/15".
Ask one question at a time: Avoid overwhelming the caller with multiple questions.
Confirm understanding: Paraphrase important information back to the caller.

## Task Instructions
[Specific steps the agent should follow]

## Objection Handling
If the caller says they're not interested: "I understand. Is there anything specific..."
If the caller is frustrated: "I hear your frustration, let me help resolve this..."

Best Practice 2: Use Conversation Flow for Complex Tasks

When your agent needs to handle complex logic or multiple tools, consider using Conversation Flow agents instead of managing everything in a single prompt.

When to switch to Conversation Flow:

• More than 3–4 conditional decision branches
• Using 5+ different functions or tools
• Tracking multiple variables throughout the conversation
• Single prompt shows inconsistent behavior

Benefits of Conversation Flow:

• Each node focuses on one specific task
• Deterministic tool calling and transitions
• Easier to debug and optimize individual steps
• More predictable agent behavior

Best Practice 3: Explicit Tool Calling Instructions

This section applies only to Single/Multi Prompt Agents. Conversation Flow Agents handle function calls deterministically through node configuration.

LLMs often struggle to determine when to call tools based solely on tool descriptions. Without explicit instructions, agents may call tools at the wrong time, miss calls entirely, or use the wrong tool.

Solution: define clear triggers. Always specify exact conditions for tool usage in your prompts and reference tools by their exact function names.

Example: Customer Service Agent

## Tool Usage Instructions

1. Gather initial information about the customer's issue.

2. Determine the type of request:
   - If customer mentions "refund" or "money back":
     → Call function `transfer_to_support` immediately
   - If customer needs order status:
     → Call function `check_order_status` with order_id
   - If customer wants to change their order:
     → First call `check_order_status`
     → Then transition to modification_state

3. After retrieving information:
   - Always summarize what you found
   - Ask if they need additional help
   - If yes, determine next appropriate action

Best practices for tool instructions:

Practice	Description
Use trigger words	List specific words/phrases that should trigger tool calls
Define sequences	Specify when tools should be called in order
Set boundaries	Clarify when NOT to call certain tools
Provide context	Explain why each tool is being called