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AWS Lambda
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Amazon DynamoDB
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Amazon VPC
AWS IAM
AWS CloudFormation
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Supported AI models on Workik
GPT 5.2, GPT 5.1 Codex, GPT 5.1, GPT 5 Mini, GPT 5, GPT 4.1 Mini
Gemini 3 Flash, Gemini 3 Pro, Gemini 2.5 Pro, Gemini 2.5 Flash
Claude 4.5 Sonnet, Claude 4.5 Haiku, Claude 4 Sonnet, Claude 3.5 Haiku
Deepseek Reasoner, Deepseek Chat, Deepseek R1(High)
Grok 4.1 Fast, Grok 4, Grok Code Fast 1
Models availability might vary based on your plan on Workik
Features
Generate CDK Stacks
AI generates production-ready AWS CDK stacks for Lambda, API Gateway, DynamoDB, VPC, and IAM.
Fix CDK Issues Fast
Flags broken dependencies, missing permissions, and misconfigured constructs before deployment failures using AI.
Document Architecture Clearly
AI explains constructs, outputs, and dependencies with clean docs and diagram-ready summaries.
Ship Multi-Env Safely
Create dev/staging/prod stacks with consistent naming, env overrides, and controlled differences.
TESTIMONIALS
Real Stories, Real Results with Workik
"It’s easier to sanity-check the infrastructure with Workik AI. I don’t have to reconstruct the architecture in my head anymore."
Aubry Tuft
Software Architect
"I usually spend way too much time setting up CDK stacks. With Workik AI, I had a Lambda stack ready in a few minutes."
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Darcy Kenny
Cloud Developer
"As a team lead, it’s been easier to keep everyone aligned. Workik AI helps me keep our AWS CDK projects consistent."
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Naina Patel
Engineering Team Lead
What are the most common use cases for Workik’s AWS CDK Code Generator?
Workik’s AWS CDK Code Generator helps developers automate repetitive infrastructure tasks, including but not limited to:
* Generating AWS CDK stacks for Lambda, API Gateway, DynamoDB, or S3 with configuration files.
* Refactoring existing CDK projects to improve performance and maintainability.
* Debugging stack errors, dependency issues, and IAM permission conflicts.
* Documenting AWS CDK constructs with readable explanations and architecture diagrams.
* Automating validation, testing, and deployment workflows through GitHub or AWS CodePipeline.
* Migrating existing CloudFormation templates into optimized AWS CDK code.
How does context-setting improve AI output in Workik for AWS CDK projects?
While adding context is optional, adding it helps the AI generate responses tailored to your project. You can add context by:
* Connecting your GitHub, GitLab, or Bitbucket repositories.
* Defining languages, frameworks, and libraries used in your AWS CDK project.
* Including database schemas for precise resource mapping.
* Importing API blueprints for structured endpoint generation.
* Uploading codebase files or common functions for better understanding of your architecture.
* Adding dynamic context or existing CDK constructs for environment-aware responses.
How does Workik AI help with IAM permissions in AWS CDK?
Workik AI traces IAM permissions back to the CDK constructs that introduce them. It explains why specific actions are added, surfaces overly broad permissions, and helps you reason about least-privilege changes before deployment.
How does Workik AI handle environment-specific configuration in AWS CDK?
Workik AI helps you reason about environment-specific configuration in CDK projects without hardcoding values. It accounts for common patterns such as context values, environment variables, and configuration files used to differentiate dev, staging, and production stacks. This makes it easier to maintain consistent infrastructure structure while safely varying naming, scaling parameters, and resource configuration across environments.
Does Workik AI understand CDK synth output and CloudFormation templates?
Yes. Workik AI helps you reason about how CDK code synthesizes into CloudFormation. It explains generated resources, logical IDs, & why specific changes appear in synthesized templates or diffs. This makes it easier to review infrastructure changes & understand what will actually be deployed before running any commands.
How does Workik AI help with choosing between L1, L2, and L3 CDK constructs?
Workik AI helps you reason about construct choice based on control, abstraction, and generated CloudFormation behavior. It explains when L1 constructs are needed for fine-grained configuration, when L2 constructs provide safer defaults, and when L3 patterns make sense at scale. This helps developers avoid fighting abstractions, over customizing high-level constructs, or locking infrastructure into brittle patterns.
Can Workik AI help refactor existing or legacy CDK stacks?
Yes. Workik AI works with existing CDK code and helps you understand how constructs, stacks, and dependencies are currently structured. It supports changes like reorganizing constructs, adjusting stack boundaries, and evolving infrastructure patterns. AI considers logical IDs, dependencies, and environment-specific config to reduce unintended resource replacement risk.
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