Prompt Engineering

What is Prompt Engineering

Prompt engineering is the practice of clearly instructing an AI on who it is, what it should do, how it should behave, and where it must stop.

In simple terms, a prompt is not just a question, it is a set of instructions that shape the AI’s role, decision-making, tone, and actions. A well-designed prompt turns an AI model into a reliable digital assistant that behaves consistently, follows workflows, respects boundaries, and delivers human-like interactions.

In IMBrace, prompt engineering becomes even more powerful because prompts are directly connected to tools, workflows, and real actions (such as registrations, booking appointments, sending emails, or updating calendars). This means a poorly written prompt can lead to confusion or errors, while a well-structured prompt creates smooth, trustworthy, and predictable user experiences.

The goal is not to make the AI sound “smart,” but to make it useful, safe, and aligned with business intent. When done correctly, prompt engineering allows teams to:

• Control AI behaviour across all conversations

• Prevent out-of-scope or risky responses

• Guide users step by step without frustration

• Build AI agents that feel professional, calm, and human

In short, prompt engineering is the foundation of every reliable AI agent in IMBrace. Everything else, including tools, automation, and intelligence builds on top of it.

Steps for Prompt Engineering

Phase 1: Behavior Settings of AI Agent

Before the agent processes a single query, you must establish its "Rules of Engagement" within the iMBrace Behavior Settings tab.

1. Guardrail Selection

For any production or customer-facing agent, Guardrails are your first point of control.

• Action: Select a guardrail framework (e.g., NVIDIA NeMo).

• Purpose: Enforce safety boundaries, prevent "jailbreaking," and ensure the agent remains strictly on-topic.

2. Specify Role & Personality

This defines the "who" and "how" of the interaction. It sets the linguistic boundaries and professional tone.c

Example:

• Identity: You are a professional, concise booking assistant.

• Data Scope: You only collect: Date, Time, Customer Name, and an optional Note.

• Tone: Guide users step-by-step, confirm details before creating the booking, and keep a polite, clear tone.

• Restriction: If a user uploads images/files, politely decline because you only support appointment booking.

Phase 2: Core Rules & Instructional Logic

This is the "brain" of the agent. In iMBrace, this section dictates the strict logic the AI must follow to complete a task.

3. Core Task Definition

• Define the mission and the Scope Guard.

Example:

• Mission: You are an AI assistant that helps users book 90-minute appointments.

• Scope Guard (MUST): If the user’s message is NOT related to booking or managing appointments (availability, creating, confirming, rescheduling, or canceling bookings), reply exactly with: “Please waiting...” and do nothing else.

• Define Pre-conditions (what is needed to start) and Post-conditions (what must happen to finish).

Example:

• Pre-conditions (Requirements to start or proceed):

  • Time Logic: Appointments must be strictly after the current time and at or after 9:00 AM.

  • Checklist Logic: The agent must maintain a running checklist of required fields: customerName, Date (YYYY-MM-DD), Time (HH:mm), and Note.

  • Availability: Before a "Create Booking" call, the agent must have successfully executed Get Booking Timeslots to verify the slot.

• Post-conditions (Requirements to finish or exit):

  • Backtracking: If a user changes a detail mid-flow, the agent must re-validate all previously collected fields before proceeding.

  • Confirmation: The agent must summarize the Date, Start/End times, Name, and Note, and receive explicit user confirmation before calling the creation tools.

  • Audit: Upon confirmation, the agent must execute Create Booking followed by Create Google Calendar Event and echo the Booking Code.

5. Tool Integration & Response Policy

List the specific iMBrace tools available and how to handle their outputs.

• Tools: Get Booking Timeslots, Create Booking, Create Google Calendar Event, Send Email, Delete Booking.

• Policy: Surface tool outputs verbatim. Never invent values. If a tool returns an error, explain briefly and offer a recovery step (e.g., show next 5 days).

6. Workflow Sequence

Workflow sequence defines the step-by-step operational path the agent follows. It serves as the bridge between the agent's Persona and the Tools it uses.

Greet & Discovery: State required fields; offer slots for today/next 5 days.

Validation: Gather Date/Time. Use Get Booking Timeslots to verify.

Verification: Gather Name and Note.

Confirmation: Summarize all details and ask, "Correct?" before any tool call.

Execution: Create Booking → Create Calendar Event → Report Result.

Follow-up: Offer email confirmation and manage changes (Reschedule/Cancel).

Phase 3: Few-Shot Examples (Instructional Patterns)

Few-shot prompting provides the AI with "Reasoning + Action" samples to ensure it follows the workflow perfectly.

Sample Conversation 1: Happy Path

User: I'd like to book a session for tomorrow at 10 AM.

Reasoning: The user provided Date and Time. I need to check availability for tomorrow (2026-02-11) at 10:00 before proceeding. I still need the Customer Name.

Agent: I can help with that! Let me check if 10:00 AM tomorrow is available. [Tool Call: Get Booking Timeslots]. That slot is free. To finalize this, may I have your full name and any optional notes for the appointment?

Sample Conversation 2: Scope Violation

User: Can you tell me what the weather is like today?

Reasoning: This request is unrelated to booking, managing, or rescheduling appointments.

Agent: I am an AI Booking Assistant and can only assist with scheduling or managing your appointments. Please ask a related question, such as checking for available timeslots or rescheduling an existing booking.

Sample Conversation 3: Integrity & Backtracking

User: Actually, change that to Friday at 2 PM. My name is Alex.

Reasoning: The user changed the date/time. I must re-validate Friday at 14:00 against availability before confirming Alex's name for that specific slot.

Agent: No problem, Alex. Let me re-verify the availability for Friday at 2:00 PM. [Tool Call: Get Booking Timeslots]. That works! To confirm: Date: Friday, Time: 14:00-15:30, Name: Alex. Correct?

Phase 4: Meta-Prompting (Continuous Improvement)

Meta-prompting is the practice of using a high-reasoning model like Google AI Studio to act as a "Prompt Architect." Instead of guessing how to fix an agent, you use Meta-prompting to scientifically refine the Core Rules.

Purpose of Meta-Prompting

Meta-prompting helps you:

• Identify why an agent failed or behaved unexpectedly

• Strengthen core rules and guardrails

• Improve consistency without increasing prompt length

• Reduce edge-case errors over time

Meta-prompting is not a replacement for good prompt design. It is a continuous improvement layer.

1. The Feedback Loop: Analyze Failures

When an agent fails, such as skipping a mandatory field, ignoring a guardrail, or failing to backtrack, capture the raw conversation log.

• Action: Paste the failed transcript and your current Core Rules into Google AI Studio.

• The Goal: Identify exactly which "Logical Gate" in your Workflow Sequence was breached.

2. The Optimizer: Structured Refinement

Use a specific "Meta-instruction" to ask the model to debug your logic. Instead of asking for a new prompt, ask for a logic patch.

• The Prompt to AI Studio: "Analyze this conversation. The agent failed to [insert failure, e.g., 'validate the name before booking']. Re-write my 'Pre-conditions' section to be more authoritative so the model perceives this step as a hard technical dependency."

• Why this works: It uses the LLM to find linguistic "loopholes" in your rules that a human might miss.

3. Integration: Injecting Improvements

Don't just replace your whole prompt. Targeted "Logic Injections" keep your agent stable.

• Action: Take the specific refined rule (e.g., a more strict Scope Guard) and update the iMBrace Core Rules section.

• Validation: Re-run the failed scenario in the iMBrace Sandbox to ensure the "Logic Patch" holds.

Conclusion: From Instructions to Intelligence

Prompt engineering is not a "one-time setup" but the ongoing evolution of your digital workforce. By following the four phases outlined in this manual, inlcuding establishing Behavior, defining Core Logic, and utilizing Meta-Prompting, you transition from a reactive chatbot to a proactive, reliable AI agent.

In the iMBrace ecosystem, your prompt is the bridge between human intent and automated action. A well-engineered agent doesn't just talk; it thinks, validates, and executes with precision.