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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AI Job Descriptions: Build a Clear Role Scorecard
Problem: The job description is vague, so there's no clear criteria for reviewing candidates.
A clear JD, must-have criteria, and a job-relevant evidence rubric.
For: Recruiter, Hiring Manager
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AI Resume Extraction: Turn CVs into Checked Candidate Data
Problem: Skills and experience have to be manually copied from resumes into a sheet.
A structured candidate sheet with missing information flagged.
For: Recruiter, HR Executive
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AI Resume-to-JD Review: Match Evidence, Not Guesswork
Problem: Actual evidence and assumptions get mixed up when comparing a candidate to the JD.
An evidence table against job criteria — not an automatic rejection.
For: Recruiter, Talent Acquisition Lead
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AI Interview Kits: Ask Consistent, Job-Relevant Questions
Problem: Interviewers ask inconsistent questions, making fair comparison difficult.
Structured interview questions and an evaluation rubric.
For: Recruiter, Hiring Manager
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AI Interview Notes: Summarize Evidence Without Inventing Traits
Problem: Useful points from an interview get lost in scattered notes.
A verified interview summary and clarification questions.
For: Recruiter, Hiring Manager
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AI Interview Scheduling: Draft Clear Coordination Messages
Problem: Drafting scheduling and rescheduling messages for interviews takes repeated effort.
Scheduling, availability, and rescheduling message templates.
For: Recruitment Coordinator, Recruiter
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AI Onboarding Checklists: Give Every Task an Owner
Problem: Tasks, owners, and deadlines get missed during new-hire onboarding.
A role-specific onboarding checklist with responsibilities and deadlines.
For: HR Operations Executive, People Operations Lead
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AI HR FAQs: Answer from Approved Policies with Sources
Problem: Employees keep asking the same policy-related questions.
A source-backed FAQ set and HR escalation rules.
For: HR Executive, HR Systems Administrator
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AI Survey Analysis: Find Themes Without Identifying Employees
Problem: It's hard to manually pull useful themes out of employee survey comments.
Anonymized aggregate themes and improvement questions.
For: HR Business Partner, People Analyst
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AI HR Reports: Explain Metrics You Have Verified
Problem: The numbers and the written explanation in the monthly HR report don't match.
Checked metrics and a factual management summary.
For: HR Analyst, HR Manager
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AI Learning Plans: Map Skills Gaps to Practical Tasks
Problem: Training plans aren't connected to employees' actual role requirements.
A role-based learning plan with practical assignments.
For: Learning & Development Specialist, HR Business Partner
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AI Candidate Updates: Keep Applicants Informed Clearly
Problem: Candidates don't consistently get updates on their recruitment stage.
Approved stage-wise communication templates.
For: Recruiter, HR Executive
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AI Payroll Input Checks: Flag Errors Before Review
Problem: Missing attendance and duplicate entries in payroll inputs get caught too late.
An input exception sheet — not the final salary calculation.
For: Payroll Executive, HR Operations Executive
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HR AI Privacy: Prepare Safe Sample Data for Everyday Work
Problem: Candidate and employee information gets shared into AI tools unnecessarily.
A synthetic/minimized dataset and a data-sharing checklist.
For: HR Manager, HR Systems Administrator
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AI Offer Letter Checks: Verify Every Approved Detail
Problem: Names, designations, or approved terms get mismatched in offer-letter drafts.
An approved-template draft with field-by-field verification.
For: HR Executive, Recruitment Coordinator
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AI Brand Voice: Make Every Draft Sound Like Your Brand
Problem: AI content feels generic and doesn't match the brand's actual voice.
A reusable brand-voice prompt kit built from approved examples.
For: Content Writer, Brand Manager
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AI Content Calendar: Plan 30 Days Around Real Goals
Problem: Daily content ideas get picked randomly, disconnected from business goals.
A calendar with audience, objective, format, and review dates.
For: Social Media Manager, Content Strategist
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AI Content Repurposing: Turn One Asset into Five Useful Formats
Problem: A webinar or blog never gets reused after it's published once.
Source-faithful post, email, carousel, script, and summary drafts.
For: Content Writer, Social Media Executive
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AI Short-Video Scripts: Write a Clear Hook and Story
Problem: A short video's hook is weak and the script is unfocused.
One focused short-video script with checked claims.
For: Creator, Video Marketer
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AI Carousel Builder: Turn One Idea into a Clear Story
Problem: A carousel has information, but no clear story or slide flow.
Slide-by-slide copy and a readable content hierarchy.
For: Social Media Manager, Designer
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AI SEO Content Briefs: Start with Intent and Evidence
Problem: A blog brief is generic and missing factual sources.
Reader questions, a source-backed outline, and a claim checklist.
For: SEO Writer, Content Strategist
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AI Keyword Clustering: Match Search Intent to Pages
Problem: A keyword list mixes intent, and the relevant website pages aren't clear.
A reviewed keyword-intent-to-page mapping.
For: SEO Executive, SEO Strategist
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AI Ad Variations: Test Different Messages, Not Random Words
Problem: Ad variants only change wording, without testable message differences.
Distinct message angles and a creative test matrix.
For: Performance Marketer, Copywriter
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AI Landing-Page Copy: Clarify the Offer Before the Click
Problem: The landing page doesn't clearly explain the offer, benefits, or next step.
Reviewed headline, benefits, and call-to-action copy.
For: Copywriter, Growth Marketer
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AI Campaign Reports: Verify the Numbers Before the Story
Problem: Spend, revenue calculations, and conclusions don't line up in the campaign report.
Checked calculations and an evidence-based performance narrative.
For: Marketing Analyst, Performance Marketer
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AI Customer Review Mining: Find Needs Behind the Comments
Problem: Useful needs and objections stay scattered across customer reviews.
An anonymized table of needs, objections, and supporting evidence.
For: Product Marketer, Customer Marketing Specialist
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AI Newsletter Drafting: Create One Focused, Useful Email
Problem: The newsletter has a lot of information but no clear purpose.
A reviewed email with clear purpose and a relevant next action.
For: Email Marketer, Content Writer
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AI Competitor Research: Build a Source-Checked Messaging Map
Problem: Competitor comparisons rely on outdated claims or assumptions.
A dated competitor-evidence matrix and differentiation questions.
For: Brand Strategist, Product Marketer
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AI Visual Consistency: Create a Reusable Brand Prompt Kit
Problem: AI-generated visuals come out in a different brand style every time.
A visual prompt kit and reviewed concept examples.
For: Designer, Social Media Manager
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AI Lead Magnets: Build a Useful Checklist for One Customer Problem
Problem: A lead magnet gives generic information instead of solving a specific customer problem.
A focused checklist with practical next steps.
For: Growth Marketer, Content Marketer
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AI Prospect Research: Build a Source-Checked Account Brief
Problem: Prospect research is scattered across tabs and important facts get missed.
Dated business facts, relevant questions, and research gaps.
For: Sales Development Representative, Account Executive
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AI Sales Emails: Personalize Without Inventing Familiarity
Problem: Sales emails are generic and disconnected from the prospect's actual context.
A reviewed first-contact email based on verified context.
For: Sales Executive, Business Development Representative
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AI Follow-Up Sequences: Stay Relevant and Know When to Stop
Problem: Follow-ups get sent randomly, with no clear stop rules.
A reviewed sequence with reply, opt-out, and stop conditions.
For: Sales Representative, Business Development Executive
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AI Discovery Questions: Understand the Need Before the Pitch
Problem: A discovery call is heavy on the pitch, light on understanding the customer's need.
A discovery agenda and role-play-tested questions.
For: Account Executive, Sales Consultant
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AI Sales Call Notes: Turn Conversations into Checked CRM Updates
Problem: CRM notes stay incomplete after a call, or never get updated at all.
A reviewed summary and next actions from a consented transcript.
For: Sales Executive, Account Manager
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AI Sales Proposals: Draft from Approved Scope and Terms
Problem: Every client proposal has to be built from scratch.
A scope-linked proposal draft with commercial checks.
For: Account Executive, Presales Consultant
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AI Stale-Deal Reviews: Identify the Missing Next Step
Problem: Deals sit in the pipeline with no clear next action or missing information.
An evidence-backed deal review and follow-up checklist.
For: Sales Manager, Account Executive
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AI CRM Cleanup: Review Duplicates Before Merging
Problem: Duplicate contact and company records make CRM reports unreliable.
A duplicate-candidate sheet with human merge approval.
For: CRM Administrator, Sales Operations Executive
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AI Objection Practice: Rehearse One Difficult Sales Conversation
Problem: Salespeople only practice objection handling live, on an actual customer call.
A role-play transcript with accuracy/listening feedback.
For: Sales Executive, Sales Trainer
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AI Demo Talk Tracks: Connect One Use Case to Verified Features
Problem: The product demo shows the same features to every prospect.
A use-case-specific demo plan with capability boundaries.
For: Solution Consultant, Account Executive
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AI Lead Routing: Send the Right Inquiry to the Right Owner
Problem: Inbound inquiries get assigned to the wrong owner or sit unattended.
Explicit routing rules and sample exception tests.
For: Sales Operations Executive, CRM Administrator
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AI Sales Reports: Explain Pipeline Numbers You Can Verify
Problem: The weekly sales report's numbers don't match the management narrative.
Checked pipeline metrics and a factual weekly summary.
For: Sales Manager, Sales Analyst
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AI Renewal Briefs: Prepare from Real Account Evidence
Problem: Account history and unresolved issues have to be gathered manually before a renewal discussion.
Verified account history and a renewal discussion brief.
For: Account Manager, Customer Success Manager
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AI Quote Checks: Verify Price Math Before Sending
Problem: Discount, quantity, or approved pricing get applied incorrectly on a quote.
An approved-rate calculation sheet and discrepancy flags.
For: Sales Operations Executive, Account Executive
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AI Mutual Action Plans: Give Every Sales Next Step an Owner
Problem: Agreed next steps, owners, and dates between buyer and seller don't get tracked.
A buyer-confirmed action-plan draft with responsibilities.
For: Account Executive, Sales Manager
Get notified when this launchesFocus & delivery boundaries, by academy
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.