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Ten Ways to Leverage Generative AI for Development on AWS
Explore ten ways you can use Generative AI coding tools to accelerate development and increase your productivity on AWS
“Generative AI coding tools are a new class of software development tools that leverage machine learning algorithms to assist developers in writing code. These tools use AI models trained on vast amounts of code to offer suggestions for completing code snippets, writing functions, and even entire blocks of code.” (quote generated by OpenAI ChatGPT)
Introduction
Combining the latest Generative AI coding tools with a feature-rich and extensible IDE and your coding skills will accelerate development and increase your productivity. In this post, we will look at ten examples of how you can use Generative AI coding tools on AWS:
- Application Development: code, unit tests, documentation, and requirements
- Infrastructure as Code (IaC): AWS CloudFormation, AWS CDK, HashiCorp Terraform, and Red Hat Ansible
- AWS Lambda: Serverless, event-driven functions
- IAM Policies: AWS IAM policies and Amazon S3 bucket policies
- Structured Query Language (SQL): Amazon RDS, Amazon Redshift, Amazon Athena, and Amazon EMR (Hive, Presto)
- Big Data: Apache Spark and Flink on Amazon EMR, AWS Glue, and Kinesis Data Analytics
- Configuration and Properties files: Amazon MSK, Amazon EMR, Amazon OpenSearch, and Jenkinsfiles
- Apache Airflow DAGs: Amazon MWAA
- Containerization: Kubernetes resources, Helm Charts, and Dockerfiles for Amazon EKS and ECS
- Utility Scripts: PowerShell, Bash, Shell, and Python
Choosing a Generative AI Coding Tool
In my recent post, Accelerating Development with Generative AI-Powered Coding Tools, I reviewed six popular tools: ChatGPT, Copilot, CodeWhisperer, Tabnine, Bing, and ChatSonic.
