Microcourses, by Academy
60 of 300 planned courses are drafted so far — each with a specific problem, a clear title, a target role, and a practical output.
AI Debugging: Find and Fix One Reproducible Bug
Problem: AI-generated code looks right, but the actual cause of a bug isn't clear.
Reproduction steps, a verified fix, and a regression test.
For: Software Developer, Application Support Engineer
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GitHub Copilot: Build One Feature You Can Test
Problem: Part of the requirement gets missed while AI builds a feature.
A small feature checked against acceptance criteria.
For: Software Developer, Full-Stack Developer
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AI Unit Tests: Catch the Missing Edge Cases
Problem: Unit tests cover normal cases, but important edge cases get left out.
Positive, boundary, and failure tests for one function.
For: Developer, QA Automation Engineer
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AI Code Review: Spot Risky Changes Before Merge
Problem: Risky changes or missing checks go unnoticed in pull-request review.
Evidence-backed review comments and verified findings.
For: Developer, Technical Lead
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AI Codebase Navigator: Understand an Existing Module
Problem: It's hard to understand what a given module does in an existing codebase.
A module map and execution flow with file references.
For: Developer, Junior Engineer
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AI Refactoring: Clean One Function Without Breaking Behavior
Problem: Cleaning up messy code breaks existing behaviour.
A refactored function with before/after passing tests.
For: Developer, Technical Lead
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AI API Tests: Check Success and Failure Paths
Problem: API testing doesn't properly check invalid requests and error responses.
A request-test matrix and response assertions for one endpoint.
For: QA Engineer, Backend Developer
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AI SQL Assistant: Write and Verify Read-Only Queries
Problem: AI's SQL query looks plausible but returns incorrect results.
A validated query and expected-result checks on a sample database.
For: Data Analyst, Backend Developer
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AI API Docs: Explain One Endpoint Accurately
Problem: API documentation doesn't match the actual implementation.
Verified request, response, and error documentation.
For: Backend Developer, Technical Writer
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AI Log Triage: Turn Errors into an Investigation Plan
Problem: It's unclear where to start investigating from long error logs.
A sanitized log summary and an ordered investigation checklist.
For: DevOps Engineer, Support Engineer
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AI User Stories: Turn Requests into Testable Tasks
Problem: A vague business request can't be converted into testable development tasks.
A user story, acceptance criteria, and a bounded task breakdown.
For: Business Analyst, Developer
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AI Pull Request Summaries: Explain What Changed
Problem: Reviewers can't tell the purpose and impact of a change from the pull-request description.
A diff-verified summary, risks, and test notes.
For: Developer, Technical Lead
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AI Data Cleanup: Build a Validated CSV Cleaning Script
Problem: Manually cleaning CSV data is repetitive and error-prone.
A cleaning script with before/after data checks.
For: Data Analyst, Python Developer
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AI Output Validation: Turn Model Replies into Checked JSON
Problem: The LLM's response doesn't follow the expected JSON format, and the application fails.
Schema validation and failure handling in a starter endpoint.
For: Backend Developer, AI Developer
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RAG Answer Check: Add Sources and Handle Unknowns
Problem: A document chatbot answers without sources and guesses on unknown questions.
Source references and an unsupported-answer fallback in a starter chatbot.
For: AI Developer, Backend Developer
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IT & Tech
Understanding, debugging, testing, and verifying AI-generated code — not just generating it. In Stack Overflow's 2025 Developer Survey, 66% of respondents found almost-correct AI solutions frustrating, and 45% said debugging AI-generated code was more time-consuming, so this first batch focuses on reliable, everyday engineering tasks.
Scope: IT-014 and IT-015 are starter-template labs, not complete application-development programs — coding/API prerequisites are shown upfront.
Reusable kit: Sample repository, prompt pattern, input fixtures, test cases, and a verification checklist.
HR & Talent
Recruitment administration, candidate information organization, interview consistency, and employee operations. LinkedIn's Future of Recruiting report highlights streamlining repetitive recruiting work, skills-based hiring, and the continued importance of recruiter judgment.
Reusable kit: Synthetic resumes, approved policy samples, editable spreadsheets, interview rubrics, and a human-review checklist.
Marketing
Generic content, inconsistent brand voice, poor repurposing, and unverified reporting. HubSpot's 2026 research highlights challenges with personalized/channel-specific content, brand values, content repurposing, and demonstrating ROI.
Reusable kit: Sample brand brief, approved source material, content templates, a creative test sheet, and a publishing checklist.
Sales & CRM
Prospect research, generic outreach, incomplete CRM records, proposal drafting, and unclear follow-ups. Salesforce's 2026 research names sales administration, prospecting bandwidth, coaching gaps, and data quality as key challenges, with 74% of sales professionals reporting a focus on data cleansing.
Reusable kit: Synthetic prospect data, consented/sample transcripts, approved offer documents, CRM templates, and a pre-send checklist.