10 ways I use the Amazon Q Developer CLI to save time

If you haven’t already started using the Amazon Q Developer CLI Agent in your terminal, this is your sign to install it right now, and get more done without the burnout.

I wrote previously about how I used Q Developer to create architecture diagrams in seconds from a CloudFormation template, but recently I challenged myself to integrate AI into every possible area of my workflow.

Avoiding burnout from day-to-day activities

I should mention that my day-to-day role focuses on content creation. This involves a lot of hands-on implementation, primarily using AWS services.

A large part of my day is currently spent designing practical exercises, building from scratch, but also operating within the constraints of a sandbox environment which has its own challenges. For instance, in our environment certain permissions are denied to prevent misuse, and each sandbox is only available for a few hours before being wiped clean, and all resources deleted.

I also have a lot of variety in my work, in terms of the AWS services I get to work with, though right now I’m working mainly with AI/ML focussed tech. This is a fast-moving space, so any help I can get in terms of lifting the cognitive load all contributes to saving my rocket fuel, avoiding burnout, and freeing my mind to think creatively.

My top ten

So here the ways that Amazon Q Developer has been most effective in lightening the load. If you work with AWS regularly, then I hope that some of these might save you some time and energy too!

1. Creating IaC templates, e.g. “convert this CloudFormation template to CDK”, or "help me write a CloudFormation template to provision this IAM policy"

2. Look up the syntax of some of the more obscure AWS CLI commands that I don't use very often e.g. “how do I stop a Comprehend sentiment detection job using the AWS CLI”

3. Perform quick troubleshooting when an error occurs e.g."I have already configured IAM permissions, so why am I seeing this error: An error occurred (AccessDenied) when calling the PutObject operation: Access Denied"

4. Generate sample data to use for testing / proof of concept e.g. "generate a csv file containing 100 examples of synthetic names and addresses" Interestingly, when I asked for 100 examples of Personally Identifiable Information (PII), Q declined. Instead it created the file and redacted the PII!

5. Help writing IAM policies, and S3 bucket policies e.g."review this IAM policy for security best practices"

6. Explain code e.g. “explain what this Python function does”

7. Quick code generation: Generate code snippets, functions, or entire files with natural language prompts e.g. “add error handling to this function”

8. Bash Command Generation - Get complex bash commands without having to remember syntax e.g. "show me how to find all files that have been modified in the last 7 days"

9. Get help with using AWS SDKs and APIs e.g. “explain how to use boto3 to interact with Amazon Bedrock”

10. Get best practice guidance e.g. “explain the best practices when setting common request body parameters for models in Amazon Bedrock”

Here's why I think the Amazon Q CLI agent is a game changer: each of these capabilities saves me significant time by providing immediate answers and guidance without having to search through documentation or switch context to one of the 44 browser tabs I currently have open. In my role, my differentiator, or unique selling point is creativity, ideas, and imagination - the Q CLI gives me the headspace to focus on exactly that, allowing me to preserve my energy for tasks that really matter.

If you have been using Amazon Q recently, I'd love to hear how it has saved you time and energy, what should I try next?