Explore AI-assisted techniques for understanding production code, identifying bottlenecks, and optimizing data structures for better performance.

Analyzing production code with AI

Structuring your AI prompt

Building effective prompts for code analysis

Profiling time performance with AI

The AI-assisted optimization cycle

Interpreting profiler output

Validating AI-suggested changes

Optimizing memory usage with AI

From symptoms to solutions

Exploring memory allocations

Evaluating AI optimization suggestions

Analyzing and Optimizing Code

Learn to use AI for building test suites, conducting security reviews, and managing dependencies in production codebases.

AI-assisted testing

Working with AI to Build Tests

Working with AI to build a test suite

Analyzing coverage with AI

Security-first development with AI

Crafting secure prompts

Options

Threat model

Secure defaults

Validation tests

Matching security concerns to AI prompts

Analyzing security findings with AI

AI-assisted dependency management

Analyze and diagnose

Suggest fixes

Generate automation

Matching dependency issues to AI approaches

Prioritizing dependency upgrades with AI

Testing, Security, and Dependencies

Apply AI to configuration-driven design, database schema mapping and query optimization, and collaborative development workflows.

Configuration-driven development with AI

Delegating config migration tasks

Reviewing AI's config architecture proposal

Catching config drift with AI

Database design and optimization with AI

Effective prompting for query optimization

Detecting duplicates in sensor data

AI as a development collaborator

AI-assisted code review

When to challenge AI's suggestion

Congratulations and next steps

Architecture, Databases, and Collaboration

AI coding assistants can do far more than write code. In this course, you'll learn to use them for the full spectrum of software engineering work. Starting with production code analysis and performance optimization, you'll move on to AI-assisted testing, security vulnerability detection, and dependency management. Then you'll apply AI to higher-level challenges: designing configuration-driven architectures, optimizing database schemas and queries, and building effective human-AI collaboration workflows. The course focuses on tool-agnostic principles and prompting strategies that work across any AI assistant, so the skills you gain apply no matter which tools you use.

AI-Assisted Coding for Developers

Learn to use AI as a senior engineering partner for code analysis, performance optimization, security, and software architecture decisions.

Advanced AI-Assisted Coding for Developers

Read pip install errors to identify version conflicts

Interpret pip-audit report to understand vulnerability severity

Recommend which package versions to upgrade for security fixes

Propose alternatives for unmaintained dependencies

Create a setup script that builds reproducible virtual environments

Generate CI configuration to validate dependencies on every commit

Matching dependency issues to AI approaches

Advanced AI-Assisted Coding for Developers

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