How generative AI on AWS helped me summarize feedback, squash bugs, and write better Python without leaving the console.

Introduction

I recently completed a hands-on lab using Amazon Bedrock, AWS's managed generative AI service, and I came away genuinely impressed. Not just by what the AI could do, but by how quickly it slotted into real development workflows.

In this article, I'll walk you through what I learned: from touring the Bedrock console to using it as a live coding assistant. Whether you're an AWS veteran or just curious about practical AI tooling, I think there's something here for you.

What Is Amazon Bedrock?

Amazon Bedrock is a fully managed AWS service that gives you on-demand access to large language models (LLMs) without having to provision servers, manage infrastructure, or train models from scratch.

It comes in two flavors:

• Serverless models : Fully managed foundation models from AWS and partner AI companies (like Anthropic, Meta, Mistral, and others)
• Marketplace models : Over 100 specialized models deployed on managed Amazon SageMaker endpoints

One thing that stood out to me: Bedrock isn't just a chat interface. It's a full AI application platform with tools for:

• Agents : Automate tasks by connecting LLMs to APIs and data sources

• Flows : Chain Bedrock tools and AWS services into end-to-end AI pipelines

• Knowledge Bases : Upload your own docs and build Q&A bots grounded in real content

• Prompt Management : Version, test, and reuse prompts across multiple applications

For this lab, I focused on the Chat / Text playground using the Amazon Nova Micro model.

Part 1: Summarizing Messy, Unstructured Feedback

The first task felt immediately practical: summarize a wall of rambling customer feedback and extract actionable improvements.

The prompt structure I used was simple but effective:

Summarize the following feedback and produce action points for fixes and improvements:

Separating the instruction from the data (with a blank line) made the prompt cleaner both for me to read and, arguably, for the model to parse.

The feedback itself was a fictional but realistic stream-of-consciousness review of a car parts app. Full of metaphors, colloquialisms, and run-on sentences. The kind of thing you'd actually get from a user interview.

What the model returned: A clean, structured list of positives and improvement suggestions search UX, cart flow, missing features like a compatibility wizard and saved parts lists, shipping cost transparency, and live support access.

Then I pushed further:

List the top three improvements that would likely have the biggest impact on customer satisfaction.

No need to repeat the context. The model remembered the conversation and narrowed the list down intelligently.

Key takeaway: Bedrock excels at distilling unstructured human language into structured, actionable output. This is genuinely useful for product teams doing requirements gathering or user research synthesis.

Part 2: Using AI as a Coding Assistant

This is where things got interesting for me as a developer.

Fixing a ZeroDivisionError

I started with a simple Python function:

def divide(x, y):
    return x / y

Calling divide(10, 0) throws a ZeroDivisionError. I asked Bedrock:

Add exception handling for dividing by zero to this Python3 function:

def divide(x, y):
    return x / y

The model returned a version using try/except blocks, specifically catching ZeroDivisionError and returning a useful message instead of crashing. Clean, idiomatic Python.

A small but important habit I developed: always specify the language version. Python 2 and Python 3 differ significantly. The more context you give the model, the more relevant the output.

Improving the Fibonacci Algorithm

Next, I worked with a classic recursive Fibonacci implementation:

def fibonacci(n):
    if n <= 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fibonacci(n-1) + fibonacci(n-2)

This works but it's slow. The time complexity is O(2^n), which means for large values of n, it becomes unusably slow very quickly.

I asked Bedrock to evaluate the performance and suggest improvements. The model came back with two solid suggestions:

1. Memoization : Cache already-computed values so they're not recalculated
2. Iterative approach : Replace recursion with a loop, dropping complexity to O(n)

I then asked Bedrock to directly compare the time complexity of both approaches. It explained the difference clearly — the recursive version re-computes the same values exponentially, while the iterative version computes each value exactly once.

Key takeaway: Bedrock is genuinely useful for algorithm analysis. Asking "can this be improved?" and "compare these two implementations" are high-value prompts for any developer trying to write more performant code.

Part 3: Understanding and Testing Unfamiliar Code

The final section was perhaps the most practically useful for day-to-day work: using AI to understand code you didn't write.

Take this function:

def middle(arr):
    while len(arr) > 1:
        del arr[len(arr) // 2]
    return arr[0]

At a glance, it's not obvious what this does. I asked Bedrock to describe it:

Describe how the following Python 3 code works:

def middle(arr):
    ...

The model walked through it step-by-step. It also flagged some issues — notably that the function mutates the original list (a side effect most callers won't expect), and that there's no error handling.

From there, I used an improved version of the function and asked Bedrock to generate a unit test:

Generate a unit test for the following Python function using Python's built-in unittest module.
The test class should have one test that tests that None is returned if the argument is None.

def find_middle_element(arr):
    ...

The model produced a proper unittest.TestCase class, which I dropped into a file in VS Code and ran immediately. It passed.

Bonus: Generating Test Data

One underrated use case generating fake data for testing:

Generate some test data for users that includes name, address, phone number, 
and widget order history. Display the test data in the JSON data format.

Within seconds, I had realistic-looking JSON I could plug straight into tests or seed scripts. The model can also format this as YAML or TOML if you prefer.

Bedrock Pricing: What You Should Know

Bedrock prices by tokens chunks of text (roughly words or word fragments) processed as input and output.

Two pricing models are available:

The chat playground also shows you latency and token counts in real time useful for estimating costs before committing to a production integration.

Model Customization (If You Need It)

Out of the box, foundation models are general-purpose. But Bedrock also supports three customization approaches:

• Fine-tuning : Adjust tone, verbosity, or vocabulary using labeled examples
• Distillation : Transfer knowledge from a large model to a smaller, faster one (up to 500% faster, 75% cheaper)
• Pre-training : Expose a model to your domain-specific data corpus

For most developer use cases, you won't need customization — the base models are powerful and flexible. But it's good to know the option exists.

Things to Watch Out For

A few honest caveats from working with Bedrock:

Hallucinations are real. LLMs sometimes generate confident-sounding output that's just wrong. Always review generated code before running it.

Responses aren't deterministic. The same prompt can yield different outputs each time. Don't expect bit-for-bit reproducibility.

Context windows are finite. Each model has a maximum context length. For very long codebases or documents, you may need to chunk your input across multiple prompts.

Prompt engineering matters. Adding context (e.g., "You are an expert AWS cloud engineer") meaningfully improves response quality. Being specific about language versions, libraries, and constraints helps too.

Final Thoughts

Amazon Bedrock made it easy to slot AI into workflows I already use — reviewing code, writing tests, making sense of user feedback. Nothing about it felt like magic. It felt like a well-calibrated tool that rewards thoughtful prompting.

If you're on AWS and haven't explored Bedrock yet, the Chat / Text playground is a zero-friction starting point. Pick a model, type a prompt, see what happens. The learning curve is low. The upside is real.

Have you used Amazon Bedrock or another LLM in your development workflow? I'd love to hear what worked (and what didn't) drop a comment below.