LLM Prompt Engineering in Synthflow

Synthflow provides comprehensive LLM prompt engineering support through multiple interfaces and tools. Whether you’re a non-technical user configuring agents through GUI forms or a developer implementing advanced prompt strategies via API, Synthflow offers the capabilities you need to optimize LLM behavior.

What is Prompt Engineering?

Prompt engineering is the discipline of providing inputs (e.g., text or images) to GenAI models to specify and confine the set of responses the model can produce. Effective prompt engineering:

Defines the agent’s role, personality, and objectives
Provides context and background information
Guides conversation flow and decision-making
Handles edge cases and objections
Ensures consistent, on-brand responses

In Synthflow, prompt engineering happens at multiple levels—from simple no-code text fields to sophisticated RAG architectures and runtime prompt injection.

Low-Code/No-Code Prompt Engineering

Synthflow provides dedicated modules for prompt engineering that require no coding. These interfaces make prompt optimization accessible to all users.

Single-Prompt Agents: No-Code Prompt Engineering

Single-Prompt Agents represent the most accessible prompt engineering interface. Configure entire agent behaviors using a single natural language prompt.

How It Works

Write a prompt that defines:

Background Information:

Company overview and mission
Target audience characteristics
Value proposition

Agent Profile:

Name and role
Goals and objectives
Personality and tone

Conversation Instructions:

Key questions to ask
Information to collect
How to handle common scenarios

Objection Handling:

Common concerns and how to address them
Edge cases requiring special responses

Example Prompt Structure

BACKGROUND INFO
Company Overview: Majestic Estates, a modern real estate firm, excels 
in delivering unique and effective home selling solutions. By integrating 
AI technology and market analysis, we ensure our clients' listings garner 
notable attention and competitive offers.
Target Audience: Homeowners looking to sell their properties, from small 
homes to large estates.
Value Proposition: We provide expert home valuations, customized marketing 
plans, and an extensive network of buyers for rapid, lucrative sales.
AGENT DETAILS
Name: Jessica
Role: AI Real Estate Agent
Goal: Initiate dialogue with potential sellers and arrange valuation 
meetings with our agents.
SCRIPT INSTRUCTIONS
1. Introduce yourself and inquire if they've considered selling
2. Discuss their interest in a professional valuation
3. Propose arranging a valuation meeting
4. Confirm appointment details
5. Express gratitude and reassure ongoing support

Best Practices

Be Clear and Specific: Ambiguous instructions lead to unpredictable results

✅ Good: “Greet customers warmly, introduce yourself as a real estate agent, and ask if they’re looking to buy or rent.”
❌ Vague: “Talk to the customer about real estate.”

Provide Context: Agents perform better when they understand who they’re speaking for and to

Guide Conversation Flow: Outline key questions and information to collect

Anticipate Objections: Prepare the agent for common concerns

Keep It Concise: Avoid overloading with excessive details (under 300 words recommended)

Test and Iterate: Review interactions and refine prompts based on actual performance

Co-Pilot: AI-Assisted Prompt Generation

The Co-Pilot feature uses GenAI to automatically generate optimized prompts:

Describe your use case in plain language
Co-Pilot generates a complete, structured prompt
Refine the auto-generated prompt to match your specific needs
Deploy and iterate

This GenAI-assisted approach accelerates prompt engineering and provides best-practice examples.

Documentation

Single-Prompt Agents - Complete no-code prompt engineering guide with best practices

Embedded Prompt Engineering in Flow Designer

For users building with the Flow Designer, prompt engineering and context engineering is embedded directly into visual nodes. Each node type provides dedicated prompt engineering interfaces.

Global Settings Node: Agent-Wide Prompt Engineering

The Global Settings node provides low-code/no-code interface for configuring agent-wide personality and behavior.

Personality & Background

Define your agent’s core identity through a structured prompt interface:

What to Include:

Agent name and role (e.g., “You are Maya, a friendly restaurant host”)
Company context and mission
Agent goals and objectives
Tone and style (friendly, professional, empathetic)
Target audience information
Legal disclaimers or compliance requirements

Example:

You are Maya, a friendly restaurant host helping callers book tables. 
You work for Foody, a popular restaurant known for excellent service 
and fresh ingredients. Your goal is to help callers find the perfect 
reservation time and ensure they have a great dining experience. 
Always be warm, patient, and helpful—even when we're fully booked.

Best Practices:

Keep under 300 words for optimal results
Be specific to prevent inconsistent behavior
Use variables with {variable_name} syntax for personalization
Focus on essentials: who, what, and how

Objections & Special Cases

Handle common objections and edge cases with dedicated prompt engineering:

When to Use:

Common objections (“Too expensive”, “I need to think about it”)
Compliance scenarios (legal disclaimers, data privacy)
Edge cases (“User asks if you’re an AI”)
Situations requiring consistent responses

How to Configure:

Define the Case: Trigger phrase or scenario
Write Instructions: How the agent should respond

Example:

Case: “The user asks if you are an AI”
Instructions: “Confirm that you’re an AI agent and highlight the benefits: you’re available 24/7, can provide instant responses, and never have hold times. Then redirect back to helping with their request.”

This low-code interface makes sophisticated prompt engineering accessible without writing code.

Documentation

Global Settings Node - Low-code/no-code agent-wide prompt engineering

Conversation Node: Variable Collection Prompts

The Conversation Node provides prompt engineering for collecting specific information from users.

How It Works

Write natural language prompts that guide how the agent asks questions and collects data:

Prompt Field: Define what to say and what information to collect

Example:

Ask the caller for their full name and email address. 
Be friendly and explain we need this to send their confirmation.

Variable Configuration: Define variables to collect (name, email, phone, etc.)

Validation Rules: Set requirements without code:

Required vs. optional fields
Format validation (email, phone number)
Value constraints (date ranges, number limits)

Multi-Variable Collection: Collect multiple pieces of information in a single conversational exchange with instructional prompts

Best Practices

Be conversational and natural in your prompts
Explain why you’re collecting information
Use validation rules to ensure data quality
Provide examples when asking for complex information

Documentation

Conversation Node - Low-code/no-code prompt engineering for variable collection

Greeting Message Node: First Impression Prompts

The Greeting Message Node provides a dedicated module for prompt engineering the agent’s opening message.

Purpose

Configure the first impression and tone-setting prompt with:

Welcome message
Agent introduction
Context setting
Variable personalization (e.g., “Hi {lead_name}, this is Maya from Acme Corp”)

Example

Hi there! Thanks for calling Foody. I'm Maya, and I'm here to 
help you book a table. How many people will be dining with us?

This specialized prompt engineering ensures consistent, engaging conversation starts.

Documentation

Greeting Message Node - Dedicated low-code/no-code module for opening prompts

Message Node: Scripted Statement Prompts

The Message Node allows prompt engineering for one-way communications:

Use Cases:

Confirmations (“Perfect! I’ve booked your table for {party_size} people”)
Instructions (“Please have your order number ready”)
Scripted statements that don’t require user response

Configure the exact message using natural language with variable injection.

Model-Specific Prompt Engineering

Synthflow provides model-specific prompt engineering guides for optimizing prompts per LLM.

GPT-5.1 Prompting Guide

The GPT-5.1 Prompting Guide covers advanced techniques for optimizing low-latency, high-quality conversational responses with GPT-5.1.

Key Differences from GPT-4

Verbosity Control: GPT-5.1 can be more verbose. Add explicit constraints:

Be concise. Respond in under two sentences because you are on a phone call.

Persistence Instructions: Reinforce task completion expectations:

1 <persistence>
2 You are an agent. Continue working until the user's query is completely 
3 resolved before ending your turn. Only yield when you are confident the 
4 problem is fully solved.
5 </persistence>

Efficiency Directives: Reduce unnecessary steps:

1 <efficiency>
2 Efficiency is critical. You have a time limit. Plan carefully, make 
3 tool calls deliberately, and verify outputs to avoid waste.
4 </efficiency>

Structured Prompts: Use sectioned format:

Role
Instructions
Tools and Constraints
Output Format and Length Requirements
Edge Cases

XML-Like Blocks: Improve instruction clarity:

1 <instruction_spec>
2 Your specific instructions here
3 </instruction_spec>

Prompt Optimization Tools

Repetition Cleanup: Use GPT-5.1 to check your prompts for:

Redundancy
Conflicting instructions
Simplification opportunities

Prompt Optimizer: OpenAI’s built-in optimizer helps refine prompts:

When asked to optimize prompts, explain which specific phrases should 
be added or removed to elicit the desired behavior.
Prompt: [YOUR PROMPT]
Desired behavior: [WHAT YOU WANT]
Undesired behavior: [WHAT TO AVOID]
What minimal edits would help the model reliably produce the desired behavior?

Documentation

GPT-5.2 Prompting Guide - Model-specific prompt engineering techniques

Retrieval-Augmented Generation (RAG): Composite Prompt Engineering

RAG represents Synthflow’s most sophisticated prompt engineering architecture, combining retrieval with generation for grounded, accurate responses.

What is RAG?

Retrieval-Augmented Generation is a composite prompt engineering architecture that:

Retrieves relevant information from external knowledge bases
Injects retrieved content into the LLM prompt
Generates responses based on both the original prompt and retrieved facts

This approach grounds LLM responses in your specific documents and data, reducing hallucinations and ensuring accuracy.

How RAG Works in Synthflow

Knowledge Base Setup

Low-Code/No-Code Configuration:

Create Knowledge Base: Navigate to Knowledge Bases → New
Add Documents: Upload PDFs, create text documents, or paste URLs
Set Trigger Conditions: Define when the agent should search the knowledge base
Attach to Agent: Link the knowledge base in General Configuration

No coding required—entirely GUI-based setup.

Runtime RAG Process

When a user asks a question:

Semantic Search: System searches knowledge base for relevant content using embeddings
Chunk Retrieval: Most relevant document chunks are identified

Prompt Injection: Retrieved information is injected into the LLM prompt:

[Original Agent Prompt]
Relevant Information:
[Retrieved Chunk 1]
[Retrieved Chunk 2]
[Retrieved Chunk 3]
User Question: [User's question]
Generate a response using the relevant information above.

Grounded Generation: LLM generates response based on retrieved facts

Benefits of RAG Prompt Engineering

Accuracy: Responses grounded in your specific documents

Prevents hallucinations by constraining the LLM to factual content
Ensures up-to-date information without model retraining

Source Attribution: Responses based on verifiable sources

Agents can reference specific documents or policies
Audit trails for compliance scenarios

Dynamic Knowledge: Update information without changing prompts

Add new documents to knowledge base
Agents automatically reference updated content

Scalability: Handle large knowledge bases efficiently

No need to include all information in prompts
Retrieve only relevant content per query

Best Practices for RAG

Document Organization:

Keep files focused on single topics
Split large PDFs into smaller, topic-specific uploads
Use clear, descriptive filenames

Trigger Conditions:

Be explicit about scenarios that should trigger knowledge base searches
Example: “When users ask about return policies, refund procedures, or warranty information”

Content Quality:

Write clear, well-structured documents
Use headings and bullet points
Avoid prompting instructions in documents (they won’t work)

Regular Updates:

Review content regularly
Remove outdated information
Test agent responses after updates

Documentation

Knowledge Base - RAG architecture explanation

Pro-Code Prompt Engineering

For developers and advanced users, Synthflow provides API-based prompt engineering capabilities.

Dynamic Prompt Injection: Runtime Customization

Dynamic Prompt Injection allows programmatic modification of agent behavior via API requests, enabling context-aware prompt engineering at scale.

How It Works

Use the Synthflow API to inject custom prompts when initiating calls:

1 POST /api/v1/calls
2 {
3   "agent_id": "agent_123",
4   "phone_number": "+1234567890",
5   "dynamic_prompt": "This is a VIP customer. Prioritize their requests and offer premium support options. Their account value is $50,000 and they've been a customer for 5 years."
6 }

The injected prompt is added to the agent’s base prompt for that specific conversation.

Use Cases

Personalization at Scale:

Inject customer-specific context from CRM
Customize agent behavior based on customer tier
Add account history or preferences dynamically

A/B Testing:

Test different conversation strategies programmatically
Rotate prompts for experimentation
Measure outcomes across variants

Context-Aware Customization:

Adjust prompts based on time of day
Modify behavior for different customer segments
Apply regional or language-specific instructions

Dynamic Business Rules:

Inject current promotions or offers
Update pricing information per call
Apply real-time business logic

Example: VIP Customer Handling

1 // Retrieve customer data from CRM
2 const customer = await crm.getCustomer(phoneNumber);
3 
4 // Inject VIP handling if applicable
5 let dynamicPrompt = "";
6 if (customer.tier === "VIP") {
7   dynamicPrompt = `
8     This is a VIP customer with lifetime value of $${customer.ltv}.
9     - Offer expedited service options
10     - Mention their loyalty rewards balance: ${customer.rewardsPoints} points
11     - Prioritize their requests and be extra attentive
12     - Offer to connect them directly with a senior specialist if needed
13   `;
14 }
15 
16 // Initiate call with dynamic prompt
17 await synthflow.createCall({
18   agent_id: "support_agent",
19   phone_number: customer.phone,
20   dynamic_prompt: dynamicPrompt
21 });

Best Practices

Keep Injected Prompts Focused: Add only relevant, call-specific context

Combine with Base Prompts: Dynamic prompts augment, not replace, base configuration

Test Thoroughly: Ensure injected prompts don’t conflict with base instructions

Monitor Performance: Track outcomes across different prompt variations

Documentation

Dynamic Prompt Injection - API-based runtime prompt engineering

GenAI Content Guardrails

Synthflow provides multiple content-anomaly guardrails to ensure LLM outputs remain appropriate and on-brand.

Filter Words: Rule-Based Content Blocking

Filter Words provide a simple, rule-based mechanism for blocking specific terms the agent should never speak.

How It Works

Configuration: Low-code interface in General Configuration

Add terms to block list
System prevents LLM from speaking those terms
Works across all conversations automatically

Use Cases:

Sensitive information placeholders (e.g., “INSERT_API_KEY”)
Inappropriate language
Competitor names
Confidential terms
Placeholder text that shouldn’t reach customers

Example:

Blocked terms:
- [CONFIDENTIAL]
- [PLACEHOLDER]
- CompetitorName
- internal_code_name

This rule-based guardrail acts as a safety net, catching unwanted content before it’s spoken.

LLM as Judge: Quality Evaluation

Analyze Calls uses an “LLM as Judge” system to evaluate conversation quality against defined criteria.

How It Works

After calls complete, a separate LLM evaluates the conversation against criteria such as:

Persona consistency: Did the agent maintain its defined personality?
Style adherence: Did responses match the specified tone?
Step compliance: Did the agent follow the conversation flow?
Repetition avoidance: Were responses varied and natural?
Objection handling: Were concerns addressed appropriately?

Benefits

Quality Assurance: Detect conversational anomalies and prompt compliance issues

Continuous Improvement: Identify patterns in agent performance

Guardrail Mechanism: Flag conversations that deviate from expectations

Prompt Optimization: Understand which prompts produce best results

This meta-evaluation approach helps ensure prompt engineering efforts produce desired outcomes.

Webhooks Security: Preventing Prompt Injection Attacks

HMAC signature validation for webhooks provides security guardrails against malicious data injection.

How It Works

Challenge: Webhooks could be exploited to inject malicious prompts via spoofed payloads

Solution: HMAC (Hash-based Message Authentication Code) validation ensures:

Webhook payloads are authentic
Data hasn’t been tampered with
Requests come from legitimate sources

Implementation:

1 const crypto = require('crypto');
2 
3 function validateWebhook(payload, signature, secret) {
4   const hmac = crypto.createHmac('sha256', secret);
5   const digest = hmac.update(payload).digest('hex');
6   return digest === signature;
7 }
8 
9 // Reject invalid webhooks before processing
10 if (!validateWebhook(req.body, req.headers['x-signature'], SECRET)) {
11   return res.status(401).send('Invalid signature');
12 }

This security guardrail protects LLM-based systems from prompt injection attacks via webhooks.

Documentation

General Configuration - Filter Words configuration
Analyze Calls - LLM as Judge quality evaluation
Webhooks Security - HMAC validation for prompt injection prevention

Prompt Engineering Best Practices

1. Start with Structure

Use a consistent prompt structure:

Role: Who the agent is
Context: Company and business information
Goal: What the agent should accomplish
Instructions: How to conduct conversations
Edge Cases: Special scenarios and objections

2. Be Specific and Concise

Clear instructions prevent unpredictable behavior
Keep prompts under 300 words when possible
Focus on essentials, avoid fluff
Use bullet points for readability

3. Provide Examples

Show the LLM what good looks like:

Example conversations
Sample responses
Desired tone and style

4. Use Variables for Personalization

Reference collected information with {variable_name} syntax:

Hi {customer_name}, I see you're calling about order #{order_id}.

5. Test and Iterate

Review actual conversations
Identify areas for improvement
Refine prompts based on real performance
Use Simulations for regression testing

6. Layer Your Prompts

Combine multiple prompt engineering techniques:

Base personality in Global Settings
Node-specific prompts in Conversation nodes
RAG for factual information
Dynamic injection for runtime context
Filter Words for content control

7. Use Model-Specific Guidance

Different models respond differently to prompts:

Follow GPT-5.1 specific guidelines for that model
Adjust verbosity instructions per model
Test prompts when switching models

8. Implement Guardrails

Protect against unwanted outputs:

Use Filter Words for blocked terms
Implement LLM as Judge for quality checks
Secure webhooks against injection attacks
Set up escalation triggers for edge cases

Summary

Synthflow provides comprehensive LLM prompt engineering support across multiple interfaces and skill levels:

Low-Code/No-Code Modules

Module	Purpose	Interface
Single-Prompt Agents	Complete agent configuration	Natural language prompt
Global Settings Node	Agent-wide personality	Structured forms
Conversation Node	Variable collection	Prompt fields with validation
Greeting Message Node	Opening message	Text field with variables
Co-Pilot	Auto-generation	GenAI-assisted

RAG Architecture

Component	Purpose	Configuration
Knowledge Base	Document storage	GUI upload
Semantic Search	Content retrieval	Automatic
Prompt Injection	Context augmentation	Automatic
Grounded Generation	Factual responses	LLM synthesis

Pro-Code Capabilities

Feature	Purpose	Interface
Dynamic Prompt Injection	Runtime customization	API
Webhooks	Data integration	API with HMAC security
Platform API	Programmatic control	REST API

Content Guardrails

Guardrail	Purpose	Type
Filter Words	Block specific terms	Rule-based
LLM as Judge	Quality evaluation	ML-based
HMAC Validation	Security	Cryptographic

Key Benefits

✅ Accessibility: No-code interfaces for non-technical users
✅ Flexibility: Multiple prompt engineering approaches
✅ RAG Support: Grounded, factual responses
✅ Guardrails: Content control and security
✅ Model-Specific: Optimized guidance per LLM
✅ Runtime Customization: Dynamic, context-aware prompts
✅ Quality Assurance: Built-in evaluation tools

Whether you’re writing your first prompt or implementing sophisticated RAG architectures with dynamic runtime injection, Synthflow provides the tools and support you need for effective LLM prompt engineering.

Complete Documentation

Single-Prompt Agents - No-code prompt engineering
GPT-5.2 Prompting Guide - Model-specific optimization
Global Settings Node - Agent-wide prompts
Conversation Node - Variable collection prompts
Greeting Message Node - Opening message prompts
Knowledge Base - RAG architecture and setup
Dynamic Prompt Injection - Runtime API customization
General Configuration - Filter Words guardrails
Analyze Calls - LLM as Judge evaluation
Webhooks Security - Security guardrails