jnaapti

AI courses for engineers and teams

Become an AI engineer who can build, test, and operate useful systems—not just call a model. Learn AI capabilities alongside the software, data, workflow, and cloud foundations that make them reliable.

Start with the work you want to do

Ground answers in evidence. Design reliable agents. Build and test software with AI. Choose a starting point below, or explore all 58 courses across 13 areas.

Discuss an AI training plan

Contact us for full course details.

Featured courses

Retrieval-Augmented Generation

6 hours · 3 modules

A generated answer may cite retrieved text that is irrelevant, outdated, or in conflict with another source. Build a small retrieval pipeline and inspect the evidence before assessing the answer. Compare search choices and require traceable support or an explicit insufficient-evidence response.

Explore course →

AI-Assisted Software Development

6 hours · 3 modules

Generated code can satisfy a prompt while expanding scope, hiding defects, or increasing review effort. Use AI assistance for one bounded repository change and keep requirements, design, review, and tests independently checkable. Measure the full change effort and turn a recurring check into a repeatable script.

Explore course →

Human–AI Workflow Design

6 hours · 3 modules

A convincing recommendation can be wrong, and an approval button does not ensure meaningful review. Design a draft-and-review workflow that assigns decision authority using consequences, reversibility, and reviewer capacity. Test how people detect errors and retain the skills needed to challenge outputs.

Explore course →

AI Security Foundations

6 hours · 3 modules

Untrusted retrieved text can try to turn an AI suggestion into a privileged tool action. Trace that path in a small mock application and define controls at input, data, and mutation boundaries. Test prevention, detection, and response evidence with harmless fixtures rather than relying on a defensive prompt alone.

Explore course →

Why engineering foundations belong here

A model can propose an answer or action. Your application still owns validation, data, permissions, and effects. These foundations teach enduring concepts through AI-focused examples, not a separate generic technology syllabus.

  • Software and language foundations: Validate generated outputs, design typed tools, and keep model suggestions separate from deterministic rules.
  • Workflow and knowledge foundations: Assign work to rules, models, and people. Preserve evidence, approval, and review capacity.
  • Data foundations: Prepare trustworthy datasets, prevent evaluation leakage, and separate authoritative records from retrieval indexes.
  • Cloud and delivery foundations: Build around services such as Amazon Bedrock, compare managed AI offerings, and control access, capacity, cost, and release changes.

Use supplied model responses and local exercises to learn the foundations without paid AI or cloud accounts. Specialist courses add deeper model integration, retrieval, agent design, and operations.

Find your starting point

Follow the preparation for your chosen course, not the whole catalog. Bring equivalent experience where you already have the skills. Courses at the same level are parallel options, not a compulsory block.

Levels follow required-course preparation, not a universal difficulty rating. Entry skills still apply: an independent entry point is not always a beginner course.

Level 1 12 courses
Level 2 26 courses
Level 3 13 courses
Level 4 6 courses
Level 5 1 courses

Choose a learning path

These suggested routes include useful depth beyond the minimum prerequisites. Skip skills you already have, and choose optional branches only when they serve your goal.

Build grounded AI applications

A grounded prototype with validated outputs, evaluation evidence, observable behavior, and bounded authority.

  1. Artificial Intelligence Foundations
  2. Applied Generative AI
  3. Software Engineering Foundations for AI Engineers
  4. AI Security Foundations
  5. Building Applications with LLMs
  6. Retrieval-Augmented Generation
  7. AI Application Evaluation
  8. Observability Foundations for AI Systems
  9. AI Observability
Engineer reliable agent workflows

A validated agent workflow with recovery, approval, and independent task-effect checks.

  1. Artificial Intelligence Foundations
  2. Applied Generative AI
  3. Software Engineering Foundations for AI Engineers
  4. Workflow Foundations for AI Automation
  5. Agentic AI Foundations
  6. Building Applications with LLMs
  7. Event-Driven Workflow Design
  8. Reliable Workflow Execution
  9. AI Security Foundations
  10. Agent Security
  11. Harness Engineering
  12. Agent Architecture and Orchestration
  13. Agent Platforms and Frameworks
  14. AI Application Evaluation
  15. Dynamic and Agentic Workflows
  16. LLM and Agent Evaluation
Build and review software with AI

A scoped, independently tested repository change using the selected coding tool.

  1. Artificial Intelligence Foundations
  2. Software Engineering Foundations for AI Engineers
  3. AI-Assisted Software Development

Choose one coding tool

Optional depth

Harness depth

  1. Applied Generative AI
  2. Building Applications with LLMs
  3. Agentic AI Foundations
  4. Harness Engineering

Formal verification depth

  1. Formal Methods for Reliable AI Systems

Application-evaluation depth

  1. Applied Generative AI
  2. Building Applications with LLMs
  3. AI Application Evaluation
Build knowledge and context systems

A source-qualified knowledge/context model and a bounded hybrid decision prototype.

  1. Knowledge Engineering Foundations for AI Systems
  2. Database Systems for AI Applications
  3. Semantic Web and Ontology Engineering
  4. Knowledge Graph Engineering
  5. Artificial Intelligence Foundations
  6. Applied Generative AI
  7. Software Engineering Foundations for AI Engineers
  8. Building Applications with LLMs
  9. Agentic AI Foundations
  10. Retrieval-Augmented Generation
  11. Context Engineering and Context Graphs
  12. Neuro-Symbolic AI
Operate AI infrastructure

A workload-driven inference decision with resource, recovery, and operational checks; select platform/data specializations only when needed.

  1. Cloud Foundations for AI Engineers
  2. Observability Foundations for AI Systems
  3. Artificial Intelligence Foundations
  4. How Large Language Models Work
  5. AI Infrastructure

Optional depth

Local-model operations

  1. Local and Small Language Models

AWS specialization

  1. AWS Engineering for AI Applications

Cloud-native and developer-platform specialization

  1. Cloud-Native Engineering for AI Workloads
  2. Platform Engineering for AI Teams

Data-platform specialization

  1. Database Systems for AI Applications
  2. PostgreSQL Development
  3. Data Engineering Foundations for AI Systems
  4. Data Platforms for AI Workloads

Vector-infrastructure specialization

  1. Database Systems for AI Applications
  2. Vector and AI Data Infrastructure
Build AI-enabled and agent-accessible web systems

A web feature with explicit interaction states, capability descriptions, access checks, and controlled release.

  1. Web Application Foundations for AI Engineers
  2. Deployment Foundations for AI Web Applications
  3. Artificial Intelligence Foundations
  4. Applied Generative AI
  5. Software Engineering Foundations for AI Engineers
  6. Building Applications with LLMs
  7. Agentic AI Foundations
  8. Web Application Development in the AI Era
  9. The Agent-Accessible Web

Optional depth

Cloudflare specialization

  1. Cloudflare for AI Applications and Agents

Content discovery

  1. Web Discovery in the AI Era

Protocol depth

  1. Agent Interoperability and Protocols

Agent-security depth

  1. AI Security Foundations
  2. Agent Security
Redesign and govern organizational work

A noncoding pilot dossier with ownership, authority, measurement, and scale-or-stop criteria.

  1. Artificial Intelligence Foundations
  2. Workflow Foundations for AI Automation
  3. Responsible AI
  4. Human–AI Workflow Design
  5. Workflow Performance Engineering
  6. AI Governance
  7. AI Risk, Compliance and Audit
  8. Owning the AI Stack
  9. AI Adoption for Organizations
Design a controlled solo-business workflow

A repeatable business workflow with draft-only actions, reviewed rules, and total-owner-effort evidence.

  1. Artificial Intelligence Foundations
  2. Workflow Foundations for AI Automation
  3. Applied Generative AI
  4. Human–AI Workflow Design
  5. Workflow Performance Engineering
  6. Owning the AI Stack
  7. AI for the One-Person Company

All AI courses, by category

Categories connect engineering foundations with AI practice. The cloud courses include Bedrock-centered AWS work and comparisons of Microsoft Foundry, Google Cloud managed AI, and Cloudflare AI services. Choose a course to see its scope, modules, and exercises; these are not interchangeable provider implementations.

Course category · 3 courses

Software Engineering Foundations

Learn software design, language semantics, and formal reasoning through generated outputs, typed tools, and agent approval boundaries. Keep model proposals separate from deterministic application rules.

  • Software Engineering Foundations for AI Engineers

    4 hours · 2 modules · Level 1

    A model-generated report must not publish itself merely because its output looks valid. Refactor a supplied AI report feature so model proposals, deterministic rules, storage, and publication have separate responsibilities. Compare object-oriented and functional designs, swap a mock model adapter, and test the approval boundary without a live model.

    View course outline

  • Programming Language Concepts for AI Engineers

    4 hours · 2 modules · Level 1

    A model-generated expression or tool request can be syntactically valid yet have the wrong types or meaning. Trace a tiny expression language from parsing to checked execution. Use supplied agent-plan and parallel-tool traces to connect language semantics, memory, and concurrency to AI engineering without executing unrestricted generated code.

    View course outline

  • Formal Methods for Reliable AI Systems

    4 hours · 2 modules · Level 2

    An agent must not publish a model-generated report using approval granted to an earlier draft. Specify the deterministic approval controller and explore a small model for stale approval, retries, and cancellation. Compare bounded model checking with a guided proof obligation without claiming to prove the model's answers correct.

    View course outline

Course category · 4 courses

AI Foundations

Understand modern AI, how models work, and where their capabilities end.

  • Artificial Intelligence Foundations

    4 hours · 2 modules · Level 1

    AI labels often hide the real decision: should a task use a fixed algorithm, a learned model, a person, or a combination? Build a practical model of how AI systems learn and generate output, then justify where each kind of AI fits and where it does not.

    View course outline

  • How Large Language Models Work

    6 hours · 3 modules · Level 2

    A model may lose useful context or produce different answers even when a request looks similar. Trace how text becomes tokens and how generation uses context, then inspect resource and sampling trade-offs. Distinguish model weights, request context, and external memory before deciding what to include or retrieve.

    View course outline

  • Applied Generative AI

    6 hours · 3 modules · Level 2

    A plausible classification can still violate the task rules or send too many cases to review. Design a structured interaction, validate its proposed tool inputs, and compare routing choices on unseen examples. Include an abstention path and measure errors and review demand rather than trusting a model's stated confidence.

    View course outline

  • Local and Small Language Models

    4 hours · 2 modules · Level 2

    Hosted inference may not fit a workload's data or operating constraints, but a local model may exceed the available machine or fail the task. Compare two small-model configurations using the same inputs and record quality, latency, memory, and operating effort. Choose local or remote inference with explicit licensing and privacy assumptions.

    View course outline

Course category · 3 courses

AI Application Engineering

Build grounded, useful AI applications—and evaluate how well they work.

  • Retrieval-Augmented Generation

    6 hours · 3 modules · Level 2

    A generated answer may cite retrieved text that is irrelevant, outdated, or in conflict with another source. Build a small retrieval pipeline and inspect the evidence before assessing the answer. Compare search choices and require traceable support or an explicit insufficient-evidence response.

    View course outline

  • Building Applications with LLMs

    4 hours · 2 modules · Level 3

    An assistant can propose a valid-looking action that is unauthorized or based on an outdated record. Build a small application that separates observation, recommendation, and state-changing commands. Validate inputs and bind human approval to the exact proposed change before recording its actual effect.

    View course outline

  • AI Application Evaluation

    4 hours · 2 modules · Level 4

    A better-looking answer or lower latency does not show that an AI application has improved. Define a task contract and compare two versions on fixed cases with independent quality checks. Include retrieval failures, reviewer disagreement, review time, and rework so that the report does not confuse faster generation with better outcomes.

    View course outline

Course category · 5 courses

AI-Assisted Software Engineering

Use AI to build better software, from requirements to tested implementation.

Tool-specific courses are provisional. Depending on demand, they may instead become modules within AI-Assisted Software Development.

  • AI-Assisted Software Development

    6 hours · 3 modules · Level 2

    Generated code can satisfy a prompt while expanding scope, hiding defects, or increasing review effort. Use AI assistance for one bounded repository change and keep requirements, design, review, and tests independently checkable. Measure the full change effort and turn a recurring check into a repeatable script.

    View course outline

  • Claude Code Engineering

    6 hours · 3 modules · Level 3

    Provisional course

    A coding tool can explore a repository and propose changes, but its plan is not permission to mutate every file. Examine a version-labeled Claude Code workflow and separate exploration, implementation, and independent review. Compare guided tool automation with an external deterministic controller using the same acceptance contract.

    View course outline

  • Codex and OpenAI Coding Agents

    4 hours · 2 modules · Level 3

    Provisional course

    A coding agent's completion message can hide an incomplete patch or a change outside the requested scope. Define a delegation contract and inspect a version-labeled Codex workflow against actual repository effects. Separate permission to propose a change from permission to commit, merge, or deploy it.

    View course outline

  • OpenCode and Open Agentic Coding Tools

    4 hours · 2 modules · Level 3

    Provisional course

    A reusable coding skill may work only because the current session contains hidden context, while parallel workers can produce conflicting changes. Examine a version-labeled OpenCode workflow and test a small scripted skill in a fresh session. Define isolated worker ownership, review capacity, and a merge decision based on artifacts rather than model agreement.

    View course outline

  • Harness Engineering

    6 hours · 3 modules · Level 4

    An agent that resumes a session can lose important context or attempt an action that was never approved. Prototype a small harness that separates model, provider, tools, policy, and durable state. Resume permitted work from an explicit checkpoint and verify that replanning cannot expand its authority.

    View course outline

Course category · 5 courses

Agentic Systems

Design agents that plan, use tools, collaborate, and operate within clear boundaries.

  • Agentic AI Foundations

    6 hours · 3 modules · Level 2

    A task may need a fixed workflow rather than an agent that chooses its next step. Simulate a bounded agent loop and distinguish generated advice, tool requests, and actual effects. Evaluate the observed result against a goal and stop or escalate when further action would exceed delegated authority.

    View course outline

  • Agent Architecture and Orchestration

    6 hours · 3 modules · Level 3

    Parallel agents can create duplicate completions, conflicting artifacts, and a review queue that grows faster than results can be checked. Design explicit worker ownership, artifact contracts, and coordinator acceptance rules. Simulate worker loss and duplicate delivery before deciding whether multiple agents are better than a single controlled loop.

    View course outline

  • Agent Interoperability and Protocols

    4 hours · 2 modules · Level 3

    Agent integrations can expose similar-looking capabilities while assigning discovery, execution, identity, and payment to different layers. Compare version-labeled interface examples and design one local integration contract. Test compatibility and authority separately without assuming every named specification has the same maturity or purpose.

    View course outline

  • Agent Execution Environments

    6 hours · 3 modules · Level 3

    An agent that can execute code or use an authenticated browser can affect more than its working directory. Select execution capabilities from a threat model and compare isolation choices before running a disposable task. Verify denial, time limits, revocation, and cleanup within the protections actually configured.

    View course outline

  • Agent Platforms and Frameworks

    4 hours · 2 modules · Level 4

    A framework can simplify tool and state handling while adding dependencies and hiding important failure behavior. Compare version-labeled platform examples and inspect the same bounded task through a framework and a custom controller. Choose from tested contract, recovery, portability, and review requirements rather than a feature list alone.

    View course outline

Course category · 6 courses

Workflow Engineering

Model work before adding AI. Assign rules, models, and people clear responsibilities; design reliable execution and measure total work, review capacity, and outcomes.

  • Workflow Foundations for AI Automation

    6 hours · 3 modules · Level 1

    AI can draft a proposal quickly while leaving a larger queue for human review. Model a request-to-proposal process before deciding which steps belong to rules, models, or people. Make approval and escalation explicit, then compare total handling and review effort rather than model speed alone.

    View course outline

  • Event-Driven Workflow Design

    6 hours · 3 modules · Level 2

    Duplicate or delayed messages can trigger invalid actions and obscure a request's history. Design a bounded event-driven request workflow with explicit commands, versioned events, and state transitions. Reconstruct historical state without replaying external effects.

    View course outline

  • Reliable Workflow Execution

    6 hours · 3 modules · Level 2

    A timeout does not tell a worker whether an external action succeeded. Execute a small report workflow with durable stage state, bounded retries, and version-checked ownership. Test recovery and compensation rather than assuming that rerunning a task is harmless.

    View course outline

  • Human–AI Workflow Design

    6 hours · 3 modules · Level 2

    A convincing recommendation can be wrong, and an approval button does not ensure meaningful review. Design a draft-and-review workflow that assigns decision authority using consequences, reversibility, and reviewer capacity. Test how people detect errors and retain the skills needed to challenge outputs.

    View course outline

  • Workflow Performance Engineering

    6 hours · 3 modules · Level 2

    Faster generation can increase waiting when reviewers cannot keep up. Compare manual, model-driven, and hybrid workflows using whole-process time, quality, setup cost, and rework. Use a bounded capacity analysis to justify a scale, simplify, or stop decision.

    View course outline

  • Dynamic and Agentic Workflows

    6 hours · 3 modules · Level 3

    A generated plan can satisfy a schema while proposing unsafe actions or invalid dependencies. Compile a proposed report plan only after checking typed stages, policy, and approval bound to its version. Compare bounded replanning with a simpler fixed workflow.

    View course outline

Course category · 5 courses

Knowledge Engineering

Turn source-qualified claims into useful AI evidence. Connect representation, retrieval, context, and explicit reasoning without treating generated text as verified knowledge.

  • Knowledge Engineering Foundations for AI Systems

    4 hours · 2 modules · Level 1

    A model may extract a skill from a profile without reliable evidence that the person has it. Model people, skills, and role requirements as source-qualified claims, concepts, and rules. Compare document retrieval with structured knowledge and keep accepted, inferred, disputed, and unknown information distinct when preparing evidence for AI systems.

    View course outline

  • Semantic Web and Ontology Engineering

    6 hours · 3 modules · Level 2

    Shared file formats do not ensure that two datasets use the same identities or meanings. Encode a small role-and-skills ontology, query its triples, and inspect a bounded inference. Apply a separate validation check so missing data and inferred types are not confused with invalid records.

    View course outline

  • Knowledge Graph Engineering

    6 hours · 3 modules · Level 2

    A graph can duplicate entities or turn unsupported extracted relationships into apparent facts. Convert a small relational dataset into one graph model with stable identities and reviewed assertions. Test repeatable ingestion, conflicting sources, and retirement of outdated claims.

    View course outline

  • Context Engineering and Context Graphs

    6 hours · 3 modules · Level 3

    A fresh agent session can lose a decision's history or act on stale evidence. Build a task-specific context package from synthetic logs, calendar records, and messages using source, time, and access rules. Preserve historical decisions separately from current knowledge.

    View course outline

  • Neuro-Symbolic AI

    6 hours · 3 modules · Level 4

    A fluent recommendation can violate explicit requirements, while a rule system can reason correctly from a false premise. Combine candidate model-extracted skill facts with reviewed domain rules and separate constraint checks. Compare accept, reject, and escalate decisions against supplied evidence.

    View course outline

Course category · 5 courses

Data Engineering and Databases

Choose authoritative stores, prepare trustworthy AI datasets, and maintain derived retrieval indexes. Connect consistency, provenance, split isolation, and recoverable updates to AI application needs.

  • Database Systems for AI Applications

    4 hours · 2 modules · Level 1

    An AI assistant may retrieve an outdated document even after the authoritative record changes. Compare stores for conversations, documents, relationships, and agent state using access patterns and consistency needs. Separate authoritative records from derived retrieval indexes and inspect concurrent updates and deletion propagation before adding more databases.

    View course outline

  • Data Engineering Foundations for AI Systems

    6 hours · 3 modules · Level 1

    A retrieval or evaluation dataset can look valid while omitting records, exposing personal data, or leaking evaluation examples into training data. Build a small source-qualified dataset pipeline with reconciliation, deduplication, and versioned transformations. Test incremental updates and split isolation before trusting downstream AI results.

    View course outline

  • PostgreSQL Development

    6 hours · 3 modules · Level 2

    A query can return correct-looking results while concurrent updates break a business invariant. Build a PostgreSQL offer feature with keys, constraints, version-checked updates, and an atomically recorded change event. Inspect query plans separately from correctness checks.

    View course outline

  • Data Platforms for AI Workloads

    4 hours · 2 modules · Level 2

    An AI dataset or retrieval corpus needs trustworthy versions and recoverable updates, not every data-platform component. Compare lake, lakehouse, batch, and stream responsibilities for a supplied AI data workload. Use curated traces to decide whether distributed processing and rapid refresh justify their operating and recovery costs.

    View course outline

  • Vector and AI Data Infrastructure

    6 hours · 3 modules · Level 2

    A vector query can run successfully while using incompatible embeddings or returning deleted content. Benchmark one local retrieval path with judged relevance, metadata filters, and versioned index contracts. Compare quality, latency, and lifecycle behavior rather than treating similarity as evidence of truth.

    View course outline

Course category · 6 courses

Cloud and Platform Engineering

Operate AI applications with explicit identity, data, capacity, and cost boundaries. Learn Bedrock-centered AWS engineering, compare managed AI offerings, and build repeatable cloud-native and developer-platform workflows.

Cloudflare may become a standalone course if demand supports it.

  • Cloud Foundations for AI Engineers

    4 hours · 2 modules · Level 1

    A hosted model API and self-hosted inference assign cost, data handling, and recovery to different owners. Design a basic AI application deployment with explicit compute, network, storage, and identity boundaries. Compare service choices at two utilization levels while accounting for token charges, rate limits, and durable application state.

    View course outline

  • Cloudflare for AI Applications and Agents

    4 hours · 2 modules · Level 1

    Provisional course

    An edge AI application needs separate roles for request handling, model inference, retrieval, and persistent agent state. Compare Workers, Workers AI, AI Gateway, and Vectorize with execution and coordination options. Simulate duplicate and concurrent requests while checking current limits rather than assuming every component has identical availability or isolation.

    View course outline

  • AWS Engineering for AI Applications

    4 hours · 2 modules · Level 2

    An AWS AI application needs more than a successful model call. Build a bounded Bedrock client against supplied API fixtures and map retrieval, identity, data, and monitoring responsibilities. Compare Bedrock foundation-model access with SageMaker AI model development/deployment, then test throttling, blocked responses, and approved tool actions without creating paid resources.

    View course outline

  • Cloud-Native Engineering for AI Workloads

    6 hours · 3 modules · Level 2

    Adding agent-worker replicas can increase a model API's throttling and grow the queue instead of completing more work. Package a supplied mock model gateway and worker service with explicit configuration and resource limits. Inspect Kubernetes reconciliation, cancellation, and queue behavior under a rate-limited synthetic dependency.

    View course outline

  • Platform Engineering for AI Teams

    6 hours · 3 modules · Level 2

    AI teams need repeatable model access, dataset versions, and evaluation checks, not just a shared API key. Build a small versioned environment path with provider/data-scope validation, preview-bound approval, and an owned exception route. Keep Terraform, GitOps, and delivery fundamentals while measuring developer waiting and support effort.

    View course outline

  • AI Infrastructure

    6 hours · 3 modules · Level 3

    An inference service can answer individual requests yet fail under load or route sensitive data incorrectly. Design a bounded gateway policy that separates resource needs, admission decisions, and fallback quality. Compare supplied memory profiles for quantization and context growth, and include input, output, and cache charges in the cost worksheet. Use synthetic load rather than treating a successful API call as a capacity guarantee.

    View course outline

Course category · 3 courses

AI Operations, Evaluation and Observability

Make system behavior visible. Measure quality, reliability, latency, and cost.

  • Observability Foundations for AI Systems

    6 hours · 3 modules · Level 1

    An AI request may complete successfully yet use stale retrieval evidence or fail during a tool call. Instrument a supplied mock retrieval-model-tool path and correlate its logs, metrics, and traces. Diagnose execution failures and retest a bounded correction while keeping service reliability separate from answer quality.

    View course outline

  • AI Observability

    6 hours · 3 modules · Level 4

    A plausible AI response can hide missing evidence, an invalid tool call, or an action that never committed. Build a redacted execution ledger that connects model stages, retrieved sources, policy versions, approvals, and actual outcomes. Use it to investigate failures and total operating effort without claiming access to hidden reasoning.

    View course outline

  • LLM and Agent Evaluation

    6 hours · 3 modules · Level 5

    A fluent completion message is not evidence that an agent changed the right state within its authority. Evaluate answer support, retrieved evidence, tool effects, and stop conditions separately. Compare two configurations on held-out fixtures and report review cost and disagreement alongside quality.

    View course outline

Course category · 5 courses

AI Security, Governance and Trust

Protect systems, establish authority boundaries, and deploy AI responsibly.

  • AI Security Foundations

    6 hours · 3 modules · Level 2

    Untrusted retrieved text can try to turn an AI suggestion into a privileged tool action. Trace that path in a small mock application and define controls at input, data, and mutation boundaries. Test prevention, detection, and response evidence with harmless fixtures rather than relying on a defensive prompt alone.

    View course outline

  • AI Governance

    6 hours · 3 modules · Level 2

    A policy has little operational value when nobody owns its exceptions or verifies its use. Create a fictional AI pilot dossier that links risk, delegated authority, human oversight, and evidence. Walk a policy change through distinct proposal, approval, deployment, observation, and revision responsibilities.

    View course outline

  • Responsible AI

    6 hours · 3 modules · Level 2

    An AI recommendation can harm people who never chose to use the system. Review a fictional feature for unequal effects, unsupported claims, data exposure, and meaningful human choice. Define a reachable challenge process and a bounded deployment decision that leaves unresolved harms visible.

    View course outline

  • Agent Security

    6 hours · 3 modules · Level 3

    An agent may discover a tool or possess a credential without permission to commit an action. Build a delegated-action contract and enforce approval against exact parameters and object versions. Test escalation, stale approval, and changed scope in a local permission gate.

    View course outline

  • AI Risk, Compliance and Audit

    4 hours · 2 modules · Level 3

    An action log can show what happened while leaving its approval and control effectiveness unproven. Reconstruct a fictional send incident using versioned policy, input, approval, and final-state evidence. Separate an established unauthorized action from an action whose approval record is missing. Produce a control review that separates design, observed operation, missing evidence, and legal questions.

    View course outline

Course category · 5 courses

AI-Native Web and Application Development

Create modern web experiences for people, AI-enabled interfaces, and agents.

  • Web Application Foundations for AI Engineers

    6 hours · 3 modules · Level 1

    An AI-generated action proposal must follow the same server rules as a browser submission. Build a small approval feature with an accessible frontend, typed tool API, and server-owned transitions. Inspect streamed progress without confusing it with a committed action, then test duplicate, stale, and unauthorized requests in a reproducible local preview.

    View course outline

  • Web Discovery in the AI Era

    4 hours · 2 modules · Level 1

    A retrieved page can be easy to find yet fail to support an answer or contain outdated claims. Audit a sample website for clear facts, stable identifiers, source attribution, and freshness. Test fixed questions locally and improve evidence availability without promising rankings, AI citations, or answer-engine inclusion.

    View course outline

  • Deployment Foundations for AI Web Applications

    4 hours · 2 modules · Level 2

    An AI web preview can pass while the release changes its model configuration or exposes provider credentials. Compare static hosting with server-side AI backends and track application, prompt, and model-configuration versions together. Test exact-release approval, recovery, and exposure cleanup in a local simulator without creating paid resources.

    View course outline

  • The Agent-Accessible Web

    6 hours · 3 modules · Level 3

    An agent can discover a website capability without having permission to invoke it. Describe and expose one local capability with typed preconditions, side-effect declarations, and explicit access checks. Compare supplied protocol material with direct APIs and browser interaction without claiming universal client support or protocol conformance.

    View course outline

  • Web Application Development in the AI Era

    6 hours · 3 modules · Level 4

    Stopping visible text does not necessarily stop backend work or undo a committed action. Build a mock AI-enabled web feature that separates generated proposals, validated artifacts, user approval, and actual effects. Keep rendering and authority on the application side, and report cancellation truthfully at each state.

    View course outline

Course category · 3 courses

AI Strategy and Adoption

Make deliberate adoption decisions, starting with workflows and real value.

  • AI Adoption for Organizations

    6 hours · 3 modules · Level 2

    Faster generation can increase review queues and make a workflow slower overall. Start with an organizational work problem and compare manual, deterministic, purchased, and hybrid improvements. Design a bounded pilot with ownership, total-effort measures, and scale-or-stop criteria rather than treating AI adoption as the outcome.

    View course outline

  • Owning the AI Stack

    6 hours · 3 modules · Level 2

    Possessing model weights or business context does not mean an organization can operate or migrate its AI system. Compare sourcing options for one task while separating harness, provider, model, and portable work artifacts. Exercise an outage or exit scenario and make lifecycle responsibility, export rights, and unresolved dependencies explicit.

    View course outline

  • AI for the One-Person Company

    6 hours · 3 modules · Level 2

    A solo business can lose scarce owner attention to checking and repairing its automations. Organize reusable business context and simulate one rules-first, draft-only workflow using synthetic records. Measure total owner effort and keep changes to routines, customer communications, and financial actions under explicit review.

    View course outline