A useful internal AI product is usually narrow, explicit, and testable. These blueprints translate workflow needs into schemas, architecture, failure behavior, maintenance ownership, and build-versus-buy decisions.
What You Will Be Able to Do
- Write explicit input, output, evidence, and review contracts.
- Scope a small v1 and reject unnecessary complexity.
- Build fixed test sets and human-review interfaces.
- Connect offline evaluation to production monitoring and rollback.
Guided Sequence
Structured AI products
- Build a Simple AI Classification Pipeline — A classifier blueprint containing the label schema, output contract, test set, thresholds, review path, and refresh policy.
- Customer Feedback Analyzer — A feedback-analysis specification with taxonomy, evidence, sampling, evaluation, review, and refresh rules.
- Build an AI Data-Extraction Tool — An extraction product specification with schema, validation, confidence, evidence, exceptions, and tests.
- Build a Document Summarizer — A summarizer specification with document boundaries, output schemas, source links, tests, and review.
User-facing assistants
- Build an LLM-Powered Spreadsheet Assistant — A spreadsheet-assistant design with read/write boundaries, formula checks, approvals, rollback, and tests.
- Build a Messaging-Based AI Assistant — A bot architecture specifying identity, message flow, permissions, limits, fallback, logging, and tests.
- Build a Small RAG Knowledge Tool — A RAG MVP specification with approved sources, retrieval tests, citations, access controls, and maintenance.
Review and evaluation infrastructure
- Build a Human-in-the-Loop Review Console — A review-console specification covering evidence, actions, permissions, queues, audit logs, and metrics.
- Build an AI Evaluation and Monitoring Harness — A harness specification with test-set versioning, result schema, release gates, monitored indicators, rollback triggers, and ownership.
Runnable labs
- Build Lab: Classification and Routing — A baseline routing specification and reproducible evidence record.
- Build Lab: Knowledge Assistant Evaluation — A small evaluation set with explicit groundedness, citation, and abstention checks.
Lesson Library
| Lesson | Level | Time | Learner output |
|---|---|---|---|
| Build a Simple AI Classification Pipeline | Intermediate | 15 min | A classifier blueprint containing the label schema, output contract, test set, thresholds, review path, and refresh policy. |
| Customer Feedback Analyzer | Intermediate | 15 min | A feedback-analysis specification with taxonomy, evidence, sampling, evaluation, review, and refresh rules. |
| Build an AI Data-Extraction Tool | Intermediate | 15 min | An extraction product specification with schema, validation, confidence, evidence, exceptions, and tests. |
| Build a Document Summarizer | Intermediate | 15 min | A summarizer specification with document boundaries, output schemas, source links, tests, and review. |
| Build an LLM-Powered Spreadsheet Assistant | Advanced | 20 min | A spreadsheet-assistant design with read/write boundaries, formula checks, approvals, rollback, and tests. |
| Build a Messaging-Based AI Assistant | Intermediate | 15 min | A bot architecture specifying identity, message flow, permissions, limits, fallback, logging, and tests. |
| Build a Small RAG Knowledge Tool | Intermediate | 15 min | A RAG MVP specification with approved sources, retrieval tests, citations, access controls, and maintenance. |
| Build a Human-in-the-Loop Review Console | Advanced | 20 min | A review-console specification covering evidence, actions, permissions, queues, audit logs, and metrics. |
| Build Lab: Classification and Routing | Intermediate | 12 min | A baseline routing specification and reproducible evidence record. |
| Build an AI Evaluation and Monitoring Harness | Advanced | 25 min | A harness specification with test-set versioning, result schema, release gates, monitored indicators, rollback triggers, and ownership. |
| Build Lab: Knowledge Assistant Evaluation | Intermediate | 12 min | A small evaluation set with explicit groundedness, citation, and abstention checks. |
Completion Standard
A lesson is complete when the stated learner output has been produced, reviewed against representative evidence, and assigned an owner or next decision. Reading the page without producing the artifact is orientation, not completion.
Practice Case
Use the Harborline Services running case when you do not have safely redacted examples from your own organization.
Where to Go Next
- Return to the Academy home
- Browse the Academy Practice Cases