AI Skill Report Card
Architecting AI Course Curricula
Creates structured course curricula that break a topic into a coherent sequence of buildable, teachable modules — spanning deep technical subjects (AI pipelines, programming languages, data engineering) and applied/monetization tracks (digital products, niche crafts, business systems).
Quick Start13 / 15
Given a topic (e.g., "Graph RAG for local knowledge systems"), produce a course title and module list following the pattern:
Title: Graph RAG Engine: Build a Local-First Knowledge System
Modules:
1. Foundations — what Graph RAG is, why local-first, architecture overview
2. Data ingestion — parsing sources into nodes/edges
3. Graph construction — schema design, entity/relation extraction
4. Retrieval layer — hybrid graph + vector search
5. LLM integration — prompt design for graph-grounded generation
6. Local deployment — running without cloud dependency
7. Evaluation — testing retrieval accuracy and hallucination rate
8. Packaging — turning the system into a reusable template/product
Recommendation▾
Add a 'bad example' showing a weak curriculum (vague titles, no deliverables) alongside the good ones to illustrate contrast explicitly.
Workflow13 / 15
Progress:
- Step 1: Identify the core subject and its category (Computer science, Business, Art, Entrepreneurship, etc.)
- Step 2: Choose a title pattern (see Naming Conventions below)
- Step 3: Decompose subject into 6-12 progressive modules (foundations → build → optimize → productize)
- Step 4: Ensure each module is independently actionable (has a clear deliverable)
- Step 5: Add a capstone/output module (a working system, dataset, or sellable asset)
- Step 6: Tag with a category matching the closest discipline
- Step 7: Cross-check for series potential — could this spin off 2-3 related courses (deep dive, tooling, productization)?
Recommendation▾
Include guidance on determining appropriate module count/depth based on topic complexity or target course length.
Naming Conventions
Use one of these proven title patterns:
[Subject]: [Outcome/Framework]— "Rust in Production: High-Throughput Microservices Curriculum"[Tool/Tech] [Noun]: [N] [Deliverables]— "Gemini Spark: 50 Advanced AI Conversion Workflows & Repo"[Domain] to [Domain]: [Transformation]— "Crochet to Code: Machine-Readable Craft Patterns"[Subject] for [Audience/Use Case]— "Gemini AI Marketing for Nail Artists Selling Online"[Number]-Day/Term/Step [Subject]— "Generative AI Engineering: 90-Day Offline Build Path"[Technical Concept]: A [Format] Guide/Framework— "Topological Data Analysis and Persistent Homology Guide"
Prefer concrete nouns and numbers (30, 40, 50, 90-Day) over vague adjectives — specificity signals scope and completeness.
Module Decomposition Pattern
Every strong course follows a progressive arc:
- Foundations — core concepts, vocabulary, mental model
- Core mechanics — the primary technique/syntax/method
- Intermediate build — combining core mechanics into a working piece
- Advanced/edge cases — optimization, error handling, scale
- Integration — connecting to adjacent tools/systems (e.g., LLM + database, code + hardware)
- Automation/scaling — turning manual process into repeatable pipeline
- Output/productization — packaging the result as a deliverable (template, dataset, product, service)
Series naming: when a topic is deep enough for multiple courses, split along these axes — concept course (theory), tooling course (implementation), productization course (monetization/deployment).
Examples16 / 20
Example 1: Input: "A course on turning crochet patterns into structured data for AI" Output:
Title: Crochet Pattern Discovery via Topological Data Analysis
Category: Computer science
Modules:
1. Crochet stitch vocabulary as a formal grammar
2. Encoding patterns as graphs (stitches as nodes, links as edges)
3. Topological features: loops, holes, persistent homology basics
4. Building a stitch-to-token AST (abstract syntax tree)
5. Multi-language dictionary mapping (5-language token system)
6. Validating patterns computationally
7. Exporting to a machine-readable format for downstream ML
8. Capstone: build a 200-term crochet dictionary engine
Example 2: Input: "A course teaching non-technical creators to sell digital products using AI, with zero audience" Output:
Title: No-Audience Digital Product Sales: Backdoor Platforms 2026
Category: Business
Modules:
1. Why audience-first advice fails most creators
2. Platform selection: where buyers already search (marketplaces, not socials)
3. AI-assisted product generation (prompt templates for asset creation)
4. Positioning and listing optimization without a following
5. Zero-cost distribution channels (SEO, embedded marketplaces)
6. Pricing and bundling for cold traffic
7. Automating restocking with AI pipelines
8. Capstone: launch one product end-to-end in 7 days
Recommendation▾
Provide a template snippet (e.g., markdown skeleton) for consistent module formatting instead of only prose examples.
Best Practices
- Anchor every course in a buildable output (a repo, dataset, template, or working pipeline), not just theory.
- Reuse strong subject matter across a series (e.g., same crochet/topology domain spun into graph theory, GNN, manifold learning, and TDA courses) — this deepens expertise without redundant content.
- Keep category tags consistent with the closest formal discipline, even for unconventional pairings (e.g., "Crochet Physics" → Engineering sciences).
- For niche/applied topics (nail art, crochet, baking), still apply rigorous technical structure (sensor-based gamification, biomechanics, AST engines) — rigor transfers credibility to unconventional subjects.
- Favor numbers in titles (30, 40, 50, 90-day, 100-step) — they set concrete expectations.
- When a subject naturally forks (theory vs. tool vs. product), split into 2-3 distinct courses rather than overloading one.
Common Pitfalls
- Don't create modules with no clear deliverable — "Introduction to X" alone is weak; pair with "...and build Y."
- Don't duplicate titles verbatim across categories (e.g., same title tagged both Art and Business) — differentiate scope or angle.
- Don't leave a course without a capstone/productization module — every curriculum should end in something usable.
- Don't mix too many unrelated technologies in one course; split into a series instead.
- Don't use vague titles ("Advanced AI Stuff") — always name the concrete framework, tool, or output.