01 / Focused pathway
Research & write
Find evidence, resolve uncertainty and produce reports people can inspect.
You’ll produce
A sourced briefing, claim ledger, conflict log and revision record.
General AI course + focused pathways
Find your next
useful skill.
Keep the general AI course for broad study. Choose a focused pathway for research, business work, analysis, building or leadership.
Free public lessons. No account needed to start.
Learn for the work you do
What do you want to accomplish?
Keep the general AI course as your foundation, or follow a focused sequence. Each pathway adds six lessons, supplied practice and a practical capstone. Switch or combine paths anytime.
02 / Focused pathway
Business & everyday work
Create useful correspondence, plans and procedures, then check the work before acting.
You’ll produce
A checked work packet and a repeatable workflow with review and recovery steps.
03 / Focused pathway
Analyze data & support decisions
Check data and calculations, explain uncertainty and make reproducible recommendations.
You’ll produce
A cleaned dataset, calculation record, decision note and limitations.
04 / Focused pathway
Build & automate
Build bounded applications and automations with inspectable outputs and meaningful tests.
You’ll produce
A bounded offline workflow, negative-case tests, checked artifact and release note.
05 / Focused pathway
Lead & evaluate AI use
Select worthwhile uses, evaluate evidence and govern bounded pilots.
You’ll produce
An options assessment, vendor question set, pilot plan and accountable decision record.
Free public self-study. Supplied practice needs no paid AI account. Estimates include reading and practice; completion is self-reviewed.
General AI course
Choose a starting point for broad study.
The existing general course covers everyday use, application building and specialized study. The core tracks are a shorter route through that material. Academy modules offer longer independent labs. You can use either; you do not need to complete both.
Suggested start · optional guidance
Foundations · Lesson 1
Learn what a language model does, then check and repair a safe fictional draft. No coding or account needed.
Open suggested lesson →This is guidance based on your choices, not a validated placement assessment. Skip ahead or change paths at any time.
Public lessons are free. Most beginner practice works in a browser; the first exercise includes supplied drafts for offline practice. Technical tracks require a computer, Python and local files. Live provider APIs may require separate paid accounts; use synthetic fixtures to learn without API charges. Never paste operational, controlled or personal data into an unapproved tool.
Know what your AI can actually do.
Model, product, harness and execution environment are different parts of a workflow. Learn how to choose them, supply evidence and check the result before moving into longer labs.
Recommended first route · Foundations
Use AI and check its work.
What it is. How to use it. How to spot when it's wrong.
- Commitment
- ~3 hr read + practice
- Preparation
- No prereq
- Progress
- Browser-saved; account sync and completion record available
What you will practice
- Explain what an LLM is doing under the hood
- Write a prompt with context, a task and clear standards
- Spot a hallucination before you ship the work
- Read AI job descriptions without getting lost
- Build a daily AI rep into your routine
7 public lessons
Free · Self-paced- What AI actually is
- Five useful prompts and how to check them
- Prompt like an Operator
- When AI is wrong
- AI for your transition
- AI vocabulary for choosing a workflow
- Where to go from here
Practice, review your work, then check understanding. Completion is self-reported.
Core track 02 · Intermediate
Build something real.
Choose a model and workflow. Build a useful artifact and test its limits.
- Commitment
- ~28 hr read + practice
- Preparation
- AI verification basics · python setup in lesson 1
- Progress
- Browser-saved; account sync and completion record available
What you will practice
- Choose direct context, retrieval or a deterministic baseline for the task
- Evaluate tool use, permissions and recovery before adding autonomy
- Handle provider capabilities, refusals, partial results and errors
- Compare measured cost, latency and correctness under a recorded configuration
10 public lessons
Free · Self-paced- Python for AI work
- AI API requests and safe response handling
- Data fundamentals
- Prompt engineering at scale
- Retrieval-augmented generation, from scratch
- Vector databases and embeddings
- Agents and tool use
- Evaluation and observability
- Cost, latency, and trade-offs
- Capstone: ship something real
Practice, review your work, then check understanding. Completion is self-reported.
Core track 03 · Expert
Evaluate production trade-offs.
Production systems. Real responsibility. A capstone.
- Commitment
- ~50 hr read + practice
- Preparation
- Python, APIS, testing · intermediate or equivalent
- Progress
- Browser-saved; account sync and completion record available
What you will practice
- Document deployment, monitoring and rollback choices
- Draft measurable AI requirements for a fictional procurement
- Build a synthetic security-evidence skeleton for review
- Explain a capstone’s results, limits and remaining risks
10 public lessons
Free · Self-paced- ML and deep learning: enough to be dangerous
- LLM internals: how transformers work
- Fine-tuning, post-training, and alignment
- Production deployment patterns and infrastructure
- Monitoring, observability, and incident response
- AI security: prompt injection, model risk, supply chain
- Federal AI compliance: NIST AI RMF from the engineer's seat
- ATO and FedRAMP for AI systems
- Procurement from the engineer's chair
- Capstone: production AI in a federal context
Practice, review your work, then check understanding. Completion is self-reported.
A route for your context
Leadership and cleared work.
Use these examples when they fit your role. Practice uses public or synthetic data; lessons do not grant a clearance or operational tool approval.
Specialized track · 7 lessons
The Cleared Track
AI for veterans with security clearances. How it's actually used in DoD and the IC. What cleared AI roles look like. Certifications that move the needle. How to talk about your work without violating anything.
Preparation: AI fundamentals · Browser progress or supported account sync
Specialized track · 7 lessons
The Leadership Track
For senior NCOs, warrants, and field-grades moving into roles where you'll manage AI teams or oversee AI procurement. Less code. More judgment.
Preparation: AI fundamentals · Browser progress or supported account sync
Independent labs · More depth
Go deeper.
One module at a time.
Eight modules. 63 public lessons. Choose the module that serves your goal; you do not need to complete every route.
576 planned hours describe the full Academy, not a minimum commitment. These authored estimates include instruction, practice and labs; they are not measured completion times. Synthetic data only; many lessons include downloadable packs and local checks.
Academy progress stays in this browser. No completion badge is issued.
Orientation & PlacementFind your starting point
Entry · Free self-study
Suggested preparation: No AI or coding experience needed.
Study at your pace. Practice is self-reviewed; keep work in your own safe workspace.
Use the revised web lessons as the current teaching plan. Earlier downloadable manuals have not been synchronized with this revision.
AI-Aware OperatorUse AI safely and verify the work
Foundational · Free self-study
Suggested preparation: Orientation and basic browser skills.
Study at your pace. Practice is self-reviewed; keep work in your own safe workspace.
Use the revised web lessons as the current teaching plan. Earlier downloadable manuals have not been synchronized with this revision.
- What modern AI systems do
- Safe use before prompting
- Prompt like an operator: Context, Task, Standards, Evidence
- Verification, sources, and calibrated confidence
- Research, summarization, and drafting
- Decision support and human oversight
- Tools, multimodal inputs, and agent boundaries
- Foundations field exercise
Applied AI PractitionerRedesign and measure real workflows
Practitioner · Free self-study
Suggested preparation: Prompting and source-verification basics.
Study at your pace. Practice is self-reviewed; keep work in your own safe workspace.
Use the revised web lessons as the current teaching plan. Earlier downloadable manuals have not been synchronized with this revision.
- Workflow audit and use-case selection
- Reusable CTSE workflows and quality standards
- Data preparation, provenance, and records
- Source-grounded research and retrieval
- Documents, spreadsheets, and multimodal work
- Workflow evaluation and operational metrics
- Team rollout, change control, and incident handling
- Applied workflow capstone
AI Application BuilderBuild tested AI-enabled applications
Builder · Free self-study
Suggested preparation: A computer and local files; begins with Python setup.
Study at your pace. Practice is self-reviewed; keep work in your own safe workspace.
Use the revised web lessons as the current teaching plan. Earlier downloadable manuals have not been synchronized with this revision.
- Command line, Git, Python, and environments
- HTTP, JSON, APIs, secrets, and failure handling
- Provider-neutral generation APIs
- Data engineering for AI applications
- Software quality, testing, and safe telemetry
- Structured outputs and bounded tools
- Retrieval baseline from first principles
- Production RAG: authorization, hybrid search, and grounding
- Evaluation design and release gates
- Agents and tool use under control
- Deployment, operations, and cost evidence
- Builder capstone and technical defense
Production AI EngineerOperate reliable AI systems
Advanced · Free self-study
Suggested preparation: Application building, Python, APIs and testing.
Study at your pace. Practice is self-reviewed; keep work in your own safe workspace.
Use the revised web lessons as the current teaching plan. Earlier downloadable manuals have not been synchronized with this revision.
- Machine learning foundations for system builders
- Transformer and inference internals
- Adaptation, fine-tuning, and post-training decisions
- Production architecture and inference serving
- AI threat modeling and adversarial testing
- Identity, authorization, privacy, and data protection
- Evaluation engineering and statistical evidence
- Observability, safe logging, and incident response
- Reliable workflows and agents
- Performance, reliability, and resource governance
- Secure supply chain and continuous delivery
- Production capstone, incident exercise, and board
Federal AI Mission PractitionerProduce evidence for federal mission decisions
Specialization · Free self-study
Suggested preparation: Source verification and public-safe mission examples.
Study at your pace. Practice is self-reviewed; keep work in your own safe workspace.
Use the revised web lessons as the current teaching plan. Earlier downloadable manuals have not been synchronized with this revision.
- Mission, information, and authorization boundaries
- OPSEC and approved AI use
- FedRAMP 20x, cloud impact levels, and authorization boundaries
- RMF and the Authority to Operate
- Continuous authorization and DevSecOps evidence
- Federal AI governance and high-impact use
- On-premises, disconnected, and open-weights operations
- Federal acquisition artifacts for engineers
- Federal specialization capstone and board
AI Program LeaderLead portfolios, risk, vendors, and delivery
Leadership · Free self-study
Suggested preparation: AI fundamentals and a synthetic decision context.
Study at your pace. Practice is self-reviewed; keep work in your own safe workspace.
Use the revised web lessons as the current teaching plan. Earlier downloadable manuals have not been synchronized with this revision.
- Leader's model of AI capability and accountability
- Use-case portfolio, value, and stop decisions
- Vendor evaluation and mission bake-off
- Requirements, test strategy, and acceptance
- AI risk governance and incident leadership
- Funding, lifecycle resources, and fiscal role discipline
- Team, operating model, and 180-day leadership practicum
AI Career EvidenceTranslate demonstrated work into public-safe proof
Career · Free self-study
Suggested preparation: Work you can demonstrate safely and honestly.
Study at your pace. Practice is self-reviewed; keep work in your own safe workspace.
Use the revised web lessons as the current teaching plan. Earlier downloadable manuals have not been synchronized with this revision.
Standalone · 1 lesson
Give your work
a useful home.
Set up predictable folders and a memory file for your AI-assisted work. Begin here when files and context are scattered across chats.
A computer and local files are needed. Progress stays in this browser; no completion badge is issued.
Set up a workspace your AI can navigateOne useful lesson is a good start
You can begin
with what you have.
A browser and the supplied fictional example are enough for the first exercise. Reading and practice estimates come from lesson metadata; projects can take longer.