# I Used Amazon Bedrock as My AI Coding Partner for a Day Here's What Happened

> Source: <https://dev.to/tidding/i-used-amazon-bedrock-as-my-ai-coding-partner-for-a-day-heres-what-happened-3ga4>
> Published: 2026-05-27 11:31:12+00:00

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

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.

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:

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

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

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.

```
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.

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

I started with a simple Python function:

``` python
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:

``` python
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.

Next, I worked with a classic recursive Fibonacci implementation:

``` python
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:

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.

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:

``` python
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:

``` python
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.

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

Two pricing models are available:

| Model | Best for |
|---|---|
On-Demand |
Light, infrequent, or unpredictable usage |
Provisioned Throughput |
Predictable, high-volume, or production use |

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

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

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.

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.

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.*