Demonstration of Prompt Engineering Part 2

This lesson demonstrates how adding structure, constraints, and examples to your prompts yields more consistent, reliable outputs. We’ll show a compact JSON schema you can require the model to return, provide few-shot examples, and present a runnable Python example for Amazon Bedrock. Use these patterns to reduce ambiguity and speed iterations.

Use a clear role, a strict output format, and representative examples. These three elements combined make prompts much more predictable and easier to integrate into applications.

Required output format

Require the model to return only valid JSON in this exact structure:

{
  "category": "",
  "priority": "",
  "sentiment": "",
  "recommended_action": ""
}

Enforcing a strict schema like this reduces parsing errors and simplifies downstream automation.

Always validate the model output as JSON before consuming it in production. If the model returns invalid JSON, fail safely and retry with clearer constraints.

Few-shot examples

Providing a couple of representative examples (few-shot) helps the model map free-text tickets to the structured fields you expect. Example 1 — Billing ticket:

Ticket: "My invoice has the wrong company name."

{
  "category": "Billing",
  "priority": "Medium",
  "sentiment": "Neutral",
  "recommended_action": "Route to billing support to correct invoice details."
}

Example 2 — Security ticket:

Ticket: "I think someone has accessed my account without permission."

{
  "category": "Security",
  "priority": "Critical",
  "sentiment": "Concerned",
  "recommended_action": "Escalate immediately to the security team and advise the user to change their password."
}

New ticket to classify:

Ticket: "I cannot log in and I need access before a client meeting in 30 minutes."

Expected machine-readable output (example):

{
  "category": "Login",
  "priority": "Critical",
  "sentiment": "Urgent",
  "recommended_action": "Immediately assist with login issue to ensure access before client meeting."
}

Why this works

Role + constraints + examples reduces ambiguity and narrows the model’s output distribution.
A fixed schema simplifies parsing and downstream routing.
Few-shot examples teach the mapping from natural language to structured fields.

Prompt components (quick reference)

Component	Purpose	Example / Notes
Role	Sets the model’s persona	`You are a support operations assistant.`
Output format	Constrains structure	Use the JSON schema shown above.
Examples	Demonstrates expected mapping	Include diverse tickets and expected outputs.
Validation	Ensures correctness	Parse and validate JSON before use.

Practical Python example (Amazon Bedrock)

Below is a compact Python snippet illustrating how to send the prompt to Bedrock (adjust the API call to your SDK version). The core idea is to send the role + prompt and then parse the returned text as JSON.

import boto3
import json

client = boto3.client("bedrock-runtime", region_name="us-east-1")

prompt = """
You are a support operations assistant.

Classify this ticket and return only valid JSON in the following format:
{
  "category": "",
  "priority": "",
  "sentiment": "",
  "recommended_action": ""
}

Ticket:
I cannot log in and I need access before a client meeting in 30 minutes.
"""

# Example using a 'converse' style call. Adjust parameter names to match your SDK version.
response = client.converse(
    modelId="amazon.nova-micro-v1:0",
    messages=[
        {"role": "system", "content": {"type": "text", "text": "You are a support operations assistant."}},
        {"role": "user", "content": {"type": "text", "text": prompt}}
    ]
)

# Parse and print the model output (structure depends on SDK/model; adapt accordingly)
# Below assumes the model's text output is available at response["messages"][0]["content"]["text"]
model_text = ""
if "messages" in response and response["messages"]:
    first = response["messages"][0].get("content", {})
    model_text = first.get("text") or first.get("body") or ""

try:
    parsed = json.loads(model_text)
    print(json.dumps(parsed, indent=2))
except Exception:
    # Fallback: print raw model text if it wasn't valid JSON
    print(model_text)

Example console run (cleaned):

$ python prompt_eng.py
{
  "category": "Login",
  "priority": "Critical",
  "sentiment": "Urgent",
  "recommended_action": "Immediate assistance required to resolve login issue before client meeting in 30 minutes."
}

What to expect from iterative prompt engineering

More consistent outputs as you refine role and examples
Faster integration because the model produces predictable, structured responses
Reusable prompt templates that work across use cases once tuned

Key takeaway: prompt engineering is an iterative discipline. Start with a role, define a strict output format, add diverse examples, validate outputs, and iterate.

A presentation slide titled "Key Takeaway." It states that prompt engineering is the process of structuring and refining prompts so models produce more reliable and useful results.

You have your prompt, inspect the result, address weaknesses, and iterate. Each refinement should yield stronger, more consistent outputs. This wraps up this short lesson on prompt engineering. Try these techniques in a hands-on lab to practice and reinforce the concepts.

A presentation slide titled "What's Next?" with the subtitle "Practicing various prompt engineering techniques LAB." On the right is a teal circular icon showing a stylized brain merged with circuit lines.

Links and references

Amazon Bedrock documentation
Bedrock SDK reference: check your SDK’s method names for “converse”, “invoke_model”, or equivalent.

Introduction to Gen AI and LLMs

Introduction to Amazon Bedrock

Getting Started With Amazon Bedrock

Foundation Models in Amazon Bedrock

Basic Concepts of Prompt Engineering

Introduction to Text Generation With Amazon Bedrock

How Foundation Models Process Input

Extract Insights Using Prompt Engineering

Working With Different Task Types

Integrating Amazon Bedrock With Other AWS Services

Build a Marketing Email Generation Application

Introduction to Retrieval Augmentation With Bedrock Knowledge Bases

Best Practices and Optimization

Governance, Safety in Responsible AI

Security

Monitoring and Logging

Taking action with Bedrock Agents

Advanced Topics (Optional)

Final Project

Wrap Up

Demonstration of Prompt Engineering Part 2

Required output format

Few-shot examples

Why this works

Prompt components (quick reference)

Practical Python example (Amazon Bedrock)

What to expect from iterative prompt engineering

Links and references

Watch Video

Practice Lab

​Required output format

​Few-shot examples

​Why this works

​Prompt components (quick reference)

​Practical Python example (Amazon Bedrock)

​What to expect from iterative prompt engineering

​Links and references

Watch Video

Practice Lab

Required output format

Few-shot examples

Why this works

Prompt components (quick reference)

Practical Python example (Amazon Bedrock)

What to expect from iterative prompt engineering

Links and references