Faysal AzizModernizing Software Development using AI
Empower your Python toolkit with prompt engineering, agentic automation & real execution - 3 days
Why this course
The days of coding in isolation are over. AI has shifted from helpful sidekick to essential teammate, fundamentally changing how we build, ship, and scale software. This isn't another surface-level overview—we're diving deep into the intersection where battle-tested development practices meet the raw power of modern AI tooling.
You'll master prompt engineering that actually works, supercharge your workflow with advanced Copilot techniques, and build autonomous agents that handle real work. We're talking production-ready skills: API integration, deployment pipelines, and the kind of AI-enhanced development that companies are scrambling to adopt.
Skip the theory lectures and marketing hype. This is about getting your hands dirty with the tools and techniques that are reshaping our industry right now.
Learning outcomes
By the end of this course, learners will:
- Design effective Python prompts for AI coding assistants
- Compare coding performance and workflows across ChatGPT, Gemini, Claude, and Manus
- Build and integrate agentic AI workflows using tools like Manus with task decomposition
- Implement Python-based automated code generation, execution & MCP integration
- Set up backend API interactions to support AI-driven modules
- Deploy small AI-powered services end-to-end using real tooling
Prerequisites
- Comfortable with intermediate to advanced Python
- Familiarity with Git/GitHub workflows
- Basic understanding of REST APIs
- Willingness to explore new AI-powered tools and SDKs
9 modules
011. AI‑Enhanced Coding: Prompt Engineering & Copilot4 topics
- Fundamentals of prompt patterns for code generation
- Context framing, function signatures, expected outputs
- Prompt sharpening for debugging, refactoring, test generation
- Hands-on lab: Refine prompts to solve live coding tasks
- GitHub Copilot integration
- Deep prompts within VS Code: "Implement BFS", "Write pytest tests"
- Compare outputs vs. manual coding
- Best practices: code suggestions, acceptance, overrides
022. Model Comparison: ChatGPT vs Gemini vs Claude vs Manus2 topics
- Performance benchmarks from 2025 data:
- ChatGPT: Swiss‑army tool—fast and versatile
- Gemini 2.5 Pro/Flash: Large context, cost‑effective
- Claude 4 Sonnet/Opus: Highest accuracy in code tasks
- Manus: Full-stack agentic execution
- Side-by-side live demos:
- Write a utility function, generate tests, catch bugs
- Measure speed, correctness, cost
033. Agentic Workflows: Task Decomposition & Autonomous Tools4 topics
- Architectures of agentic AI (task planners, executors, sandboxes)
- Manus in action: multi‑agent orchestration
- Hands-on lab: Decompose a task into sub‑tasks and orchestrate via a Python agent
- Introduce MCP concept, libraries, server patterns
044. MCP: Model Context Protocol for Code Execution3 topics
- What is MCP? Standardizing LLM-to‑tool links
- Building MCP servers in Python
- Registering functions with @mcp.tool()
- Executing LLM-generated code in safe environments
- Use cases: file read/write, API access, computation
055. Building a Python MCP Code Executor3 topics
- Walkthrough: mcp_code_executor GitHub project
- Support for incremental code execution, dependency installs
- Lab: Build a mini MCP executor, connect ChatGPT/Claude agents
066. Autonomy at Work: End-to-End Code Generation & Execution3 topics
- Full pipeline from prompt → code → execution → output
- Safety strategies: sandboxing, test wrappers, execution logs
- Hands-on: Build an AI-driven mini-service (e.g., currency converter or basic scraper)
077. Backend Integration: API Calls & Deployment4 topics
- Crafting effective API‑call prompts
- Embed AI-generated code in Flask/FastAPI backends
- Deploy via Docker or simple managed services
- Testing & validation workflows
088. Automating with MCP Agents & GitHub3 topics
- GitHub Copilot + MCP: commit scripts, CI interactions
- Scheduled agentic tasks: run tests, lint, deploy
- Agent logs and iterative debugging
099. Putting It All Together: Final Project2 topics
- Idea: A Python-based AI agent that reads a spec, writes code, executes tests, logs results, and deploys
- Stepwise task decomposition, MCP integration, model selection - Wrap-up: Tech stack recap, tool pros/cons, recommended AI workflows
Instructor Profile
Your instructor brings 30+ years of real-world software engineering experience, pairing foundational architecture and systems insight with the latest in AI-driven development. Expect industry-demanded skills, not academic hypotheticals.
A programme built around your team.
Share your training goals and requirements.