Highlights

• A beginner-friendly guide to launching your Generative AI journey with AWS Bedrock
• Hands-on walkthrough of creating an AI agent using Lambda and foundation models
• Real-world use case: calculating retail store revenue through Bedrock-integrated AI
• Best practices for prompt engineering and function control
• InRhythm’s commitment to building a culture of learning and best practices in software engineering

From Curiosity to Code: An AI Journey

Like many developers today, early interactions with AI were surface-level: casual questions to ChatGPT, search optimization with Gemini, and tinkering with GitHub Copilot.

This post outlines the journey a recent one of our engineers made — from first principles of Generative AI to building and testing real agents using AWS Bedrock, complete with code snippets, tools, and best practices you can immediately apply.

What is Generative AI?

At its core, Generative AI refers to AI systems capable of creating new content — text, images, music, and even code. Unlike traditional AI that recognizes patterns, generative models like Claude, Jurassic, and Amazon Titan can generate human-like outputs based on structured inputs.

This paradigm shift opens the door for automation, creativity, and enhanced developer productivity.

Introducing PartyRock: No-Code AI Experimentation

 PartyRock is a no-code AI playground built on AWS Bedrock. Think of it as a hands-on introduction to AI apps with zero setup.

Here’s what you can try:

• AWS Exam Practice Tool — Simulate exam prep with generative questions
• Elevator Pitch Generator — Auto-generate startup-style pitches from keywords
• Prompt Engineering Guide — A gamified tutorial to master effective prompt creation

Understanding AWS Bedrock: The Engine Behind the Magic

AWS Bedrock is a fully managed service that allows developers to build and scale generative AI applications with zero infrastructure management.

Key Features:

• API-first access to top-tier foundation models
• Support for multiple providers (Anthropic, Meta, Amazon, Mistral)
• Seamless integration with the AWS ecosystem
• Create AI Agents that can call custom Lambda functions

Building Your First AI Agent (In 3 Steps)

Let’s build a retail store revenue calculator using Bedrock. This is where your GenAI skills meet practical backend logic.

Step 1: Request Model Access

Navigate to Amazon Bedrock > Model Access and request access to foundation models:

• Choose models like Claude 3 Sonnet or Jurassic Mid
• Add business justification if needed
• Approval is typically quick

Step 2: Create Your AI Agent

• Go to Agents > Create Agent
• Assign a name (e.g., RetailRevenueAgent)
• Choose a model (e.g., Claude 3 Sonnet)
• Define behavior:
  “You are a helpful assistant who calculates daily revenue for retail stores.”

Step 3: Connect Logic with Lambda

Here’s where it gets interesting — you’re in control of the agent’s responses via a Lambda function.

Example: Lambda Function (Python)

def lambda_handler(event, context):
    amount1 = event['amount_sold_1']
    price1 = event['price_1']
    amount2 = event['amount_sold_2']
    price2 = event['price_2']
    
    total_revenue = (amount1 * price1) + (amount2 * price2)
    return { 'daily_revenue': total_revenue }

Define this Lambda in your Action Group and link it to the agent. This gives you total control — no hallucinated numbers, just your code doing the math.

Testing and Debugging

You can test queries like:

“Today I sold 100 apples at $2 each and 200 oranges at $3 each. What is my daily revenue?”

Your agent calls the Lambda function and returns:
“Your total daily revenue is $800.”

But what if Bedrock tries to answer without using your function? Use Show Trace to inspect execution steps and ensure that the Lambda was called.

Pro Tips and Best Practices

Prompt Engineering

• Add context to reduce hallucinations
• Use structured prompts with clear roles and expected outputs
• Test across models — each one responds differently

Agent Design

• Clearly define agent roles and responsibilities
• Use Lambda for deterministic logic
• Leverage Action Groups for modular code reuse

Next-Level Ideas

• Incorporate fruit spoilage modeling for more realistic revenue projections
• Add dynamic pricing or inventory thresholds
• Build frontend UIs with React + AppSync + Bedrock for full-stack integration

Resources to Go Further

If you’re ready to commit to your AI journey, check out this highly recommended course:

• Artificial Intelligence A-Z (2025 Edition)
  Covers GenAI, Agentic AI, Reinforcement Learning (RL), and more — great for transitioning from hobbyist to professional AI practitioner.

InRhythm’s Commitment to AI and Engineering Excellence

At InRhythm, we believe in empowering engineers to explore the cutting edge — from AI and cloud architecture to frontend innovation and DevOps automation. Our learning culture is grounded in best practices, continuous experimentation, and sharing knowledge across teams and communities.

We don’t just build software — we grow engineers, and our work with generative AI is a testament to that mission.

Final Thoughts

Generative AI is not just for researchers or data scientists anymore. With AWS Bedrock, developers of all levels can create powerful AI tools without managing infrastructure.

Whether you are debugging Lambda calls or crafting prompt templates, this is the moment to start experimenting. And as always, InRhythm is here to help you grow, learn, and lead the way forward.