AI & Machine Learning🎯 Leadership & StrategyLearnAspire Certified

AI-Assisted Software Development: Redesign Your Workflow, Ship Production Code

“Stop prompting. Start shipping. AI workflows for real developers.”

Configure, chain, review, and govern AI tools across the full sprint cycle — from ticket to merged PR

Intermediate11h6 modules36 slides18 exercises24 quiz Qs✓ Verified Mar 2026
🔥 Launch Price — 63% off. Limited time.
₹2,999₹7,999

One-time · Lifetime access · Certificate included

Sign in to Enroll
7-day money-back guarantee
  • ✓6 modules of content
  • ✓36 concept slides
  • ✓18 practical exercises
  • ✓24 quiz questions
  • ✓Capstone project
  • ✓LearnAspire certificate

Learning Outcomes

What you'll learn

→You will be able to configure a context-aware AI coding environment in VS Code or JetBrains IDEs — including workspace instructions, tool selection criteria, and .github/copilot-instructions.md or Cursor rules — tuned to your actual codebase conventions
→You will be able to generate production-candidate code for a real feature ticket using structured multi-turn prompt chaining, including decomposition, constraint injection, and adversarial validation steps that catch hallucinated APIs and logic errors before they reach review
→You will be able to run an AI-assisted code review cycle on a live pull request — identifying where AI-generated diffs introduce subtle correctness bugs, license-ambiguous suggestions, and security anti-patterns that automated linters miss
→You will be able to diagnose and recover from the four critical AI failure modes — hallucinated dependencies, stale API references, prompt injection in AI-augmented pipelines, and review fatigue drift — using structured verification checklists integrated into your CI workflow
→You will be able to deliver a two-page AI tooling adoption playbook to your engineering manager or team, covering tool selection trade-offs, guardrail configuration, onboarding sequence, and measurable success criteria grounded in your team's actual sprint velocity data

The day after you finish

The day after completing this course, you will open a real sprint ticket, generate a production-candidate implementation using structured prompt chaining, run it through your AI-assisted review checklist, catch at least one non-obvious failure the raw AI output introduced, and hand your engineering manager a one-page summary of the workflow with the governance guardrails your team needs to adopt it safely.

Who this is for

  • Mid-level to senior software developer with 3-8 years experience already using Copilot or ChatGPT ad hoc who needs to move from casual usage to structured workflow integration
  • Tech lead or senior engineer who has been asked by their manager to evaluate, standardize, or govern AI tooling adoption across a team
  • Staff engineer or architect assessing where AI-assisted development breaks down at scale — hallucinations, license risk, review fatigue, prompt injection — and needs a defensible governance model

Prerequisites

  • Daily professional use of git, GitHub PRs, and at least one branch-based team workflow — you know what a rebase conflict costs you
  • Working familiarity with at least one strongly-typed or compiled language (TypeScript, Go, Java, Python with type hints, C#, Rust) and its standard linter/formatter toolchain
  • At least 30 days of hands-on use of any AI coding assistant (GitHub Copilot, Cursor, ChatGPT, Cody, or equivalent) — this course does not explain what a language model is

Curriculum

6 modules · full breakdown

🐍 Part of: Python & IT Automation Path

Step 1 — First Script
→Step 2 — Production
→Step 3 — AI Workflow
→Step 4 — Agents
← Previous: Step 2 — ProductionNext in path: Step 4 — Agents →
🏆

Capstone Project

Sprint-to-Merge: AI Workflow Audit and Team Adoption Playbook

Working against a provided GitHub repository containing a realistic legacy TypeScript/Node or Python service with at least three open issues tagged as backlog tickets, the learner selects one medium-complexity ticket, executes the full AI-assisted development workflow taught in the course (context configuration, prompt chain, generation, validation, AI-assisted self-review), then audits the resulting PR diff to document every AI failure mode encountered and every intervention made. They then produce a two-page Team Adoption Playbook as a Markdown document committed to the repo, covering: their IDE and tool configuration decisions with rationale, the prompt chain template they used and where it required correction, a failure mode log with mitigations, and a proposed onboarding sequence and success metrics their team could implement in the next two-week sprint.

What you'll deliver

A GitHub pull request against the capstone repository containing: (1) working code diff that passes the existing test suite and CI pipeline, (2) a PR description written using the AI-assisted review template from Module 5, (3) a PLAYBOOK.md file in the repo root containing the two-page Team Adoption Playbook, and (4) an inline code comment log annotating every point where AI output was accepted, rejected, or corrected and why

Sign in to Enroll →