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Introduction

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 focuses on 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

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Getting Started

To see examples of effective prompts, create a new agent in the Dashboard and explore our pre-built templates. These templates demonstrate best practices for various use cases.

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Best Practice 1: Use Sectional Prompts

Break large prompts into focused sections for better organization and LLM comprehension. This structured approach offers several benefits:
  • Reusability: Sections can be adapted across different agents
  • Maintainability: Easy to update specific behaviors without affecting others
  • Clarity: LLMs process structured information more accurately
## 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..."

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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 trying to manage everything in a single prompt.

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When to Switch to Conversation Flow:

  • Multiple decision branches: More than 3-4 conditional paths
  • Tool coordination: Using 5+ different functions/tools
  • State management: Tracking multiple variables throughout the conversation
  • Reliability concerns: Single prompt shows inconsistent behavior

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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

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Best Practice 3: Explicit Tool Calling Instructions

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

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The Challenge

LLMs often struggle to determine when to call tools based solely on tool descriptions. Without explicit instructions, agents may:
  • Call tools at inappropriate times
  • Fail to call tools when needed
  • Use the wrong tool for a situation

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Solution: Define Clear Triggers

Always specify exact conditions for tool usage in your prompts. Reference tools by their exact names.

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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

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Best Practices for Tool Instructions

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