AI Software Development: From First Prompt to Production Code by Mihail Eric is a practical training program for software developers, engineering managers, and technical teams who want to integrate AI into real-world development workflows.

Rather than focusing on basic demos or isolated coding exercises, the course teaches the complete process of using AI agents to research, plan, build, test, and ship production-ready software. Students learn how to configure an AI-native development environment, provide better context to coding agents, reduce unreliable outputs, and coordinate multiple agents across a shared codebase.

Taught by a former Amazon AI lead and Stanford lecturer, the program is designed to help developers use AI more effectively throughout the software development lifecycle.

Key Benefits

✅ Build real software features with AI agents

✅ Create an AI-native development workflow

✅ Improve the quality of AI-generated code

✅ Reduce hallucinations and implementation errors

✅ Master advanced prompting techniques

✅ Apply context engineering to complex projects

✅ Coordinate multiple AI agents on one codebase

✅ Move from initial idea to production deployment

What You'll Learn

AI-Native Development Foundations

Understand how artificial intelligence changes the modern software development process.

You'll learn:

• AI-assisted software development
• Coding agent capabilities
• Human and AI collaboration
• AI development workflows
• Appropriate AI use cases
• Common limitations and risks

Development Environment Configuration

Set up an AI-native coding environment for your preferred tools and technology stack.

Topics include:

• AI coding tools
• Agent configuration
• Repository setup
• Development instructions
• Tool integrations
• Workflow customization

Research and Feature Planning

Use AI agents to investigate codebases, clarify requirements, and create stronger implementation plans.

You'll cover:

• Codebase exploration
• Technical research
• Requirement analysis
• Feature decomposition
• Architecture planning
• Implementation roadmaps

Advanced Prompting for Software Development

Learn how to communicate technical requirements clearly to AI coding agents.

Includes:

• Prompt structure
• Task specification
• Constraint setting
• Output evaluation
• Iterative prompting
• Debugging instructions

Context Engineering

Provide AI agents with the information they need to produce accurate and relevant code.

You'll discover:

• Context selection
• Repository documentation
• Project rules
• Dependency awareness
• Technical constraints
• Context window management

Production Code Implementation

Move beyond toy examples and use AI to develop reliable software features.

Topics include:

• Feature development
• Code generation
• Refactoring
• API implementation
• Application logic
• Production standards

Reducing AI Hallucinations and Errors

Improve the reliability of AI-generated code through better instructions, validation, and review.

You'll learn:

• Error prevention
• Output verification
• Assumption detection
• Code review workflows
• Guardrails
• Iterative correction

Testing and Quality Assurance

Use AI agents to create, improve, and execute software tests.

You'll cover:

• Unit testing
• Integration testing
• Test case generation
• Edge case discovery
• Regression testing
• Quality validation

Working with Multiple AI Agents

Coordinate multiple agents across research, coding, testing, and review tasks.

Includes:

• Agent task delegation
• Parallel development
• Shared context management
• Code conflict prevention
• Agent specialization
• Workflow coordination

Continuous Integration and Delivery

Integrate AI-assisted development into modern software delivery systems.

You'll discover:

• CI workflows
• Automated testing
• Code review processes
• Deployment preparation
• Production checks
• Release management

Engineering Management with AI

Learn how engineering leaders can introduce AI into team workflows without sacrificing code quality.

Topics include:

• Team adoption
• Development standards
• AI usage policies
• Productivity measurement
• Code quality control
• Process improvement

Included Resources

Practical Course Materials

• End-to-end AI development workflows
• Real-world software examples
• Prompting frameworks
• Context engineering techniques
• Agent configuration guidance
• Production implementation methods

Applied Development Training

• Research exercises
• Planning workflows
• Coding demonstrations
• Testing systems
• Multi-agent processes
• Continuous integration strategies

Who This Program Is For

Software Developers

Use AI agents to research, build, test, and ship software more efficiently.

Engineering Managers

Introduce structured AI workflows across development teams.

Technical Founders

Build and improve software products with AI-assisted development systems.

Senior Engineers

Coordinate AI agents across complex codebases and production environments.

Development Teams

Create consistent standards for using AI throughout the software lifecycle.

AI-Assisted Coders

Move beyond basic prompting and learn production-level development practices.

What Makes This Course Different?

Many AI coding courses focus on small demonstrations, isolated prompts, or simple applications. AI Software Development: From First Prompt to Production Code focuses on the complete production workflow.

The course covers how to use AI agents from the earliest research and planning stages through implementation, testing, continuous integration, and final delivery. It also emphasizes context engineering, error reduction, agent coordination, and environment configuration for real software projects.

This makes the program suitable for developers who want to use AI as part of a reliable engineering process rather than as a basic code-generation shortcut.