Course Objective & Scope
In finance, legal, healthcare, HR and enterprise operations, users cannot act on an AI output unless they can see which source and logic produced it. Yet dumping raw chain-of-thought logs or twenty unverified links onto the screen overwhelms users rather than explaining anything.
This course teaches how to design inline citations and source previews in RAG, contributing-factor breakdowns in scoring/recommendation models and digestible reasoning summaries in agents, culminating in an explainability prototype and acceptance plan for your product.
Who Should Attend?
- Product managers building decision-support, RAG, analytics and enterprise AI products
- UX/UI designers and information architects
- Product and compliance teams in regulated industries (banking, insurance, healthcare, public sector)
- No coding prerequisites
Key Takeaways
- Tailor explanation depth to the audience (end user, domain expert or compliance auditor)
- Visually link generated claims to exact source paragraphs, table cells or bounding boxes
- Present reasoning-model steps using progressive disclosure without cluttering the interface
- Design 'Why this?' and counterfactual ('What would change the outcome?') explanations for scoring and recommendations
- Evaluate citation faithfulness to prevent illusory explainability
Curriculum
Why Explainability Matters and Who It Serves
- Distinguishing the needs of domain experts, end users and compliance auditors
- Progressive disclosure architectures for layered explanations
Inline Citations and Source Provenance
- Sentence-level citation badges, hover passage previews and side-by-side bounding-box highlights
- Explaining structured SQL/API queries via human-readable filter summaries
Reasoning Summaries and Calculation Transparency
- Presenting reasoning-model steps in collapsible, scannable summaries
- Surfacing explicit assumptions, formulas and excluded data points
Decision Support, Scoring and Regulatory (EU AI Act) Alignment
- Key contributing factors and counterfactual ('What would change this?') explanations
- Transparency and human-oversight obligations in high-risk AI workflows
Preventing Illusory Explanations and Measuring Quality
- Detecting citation hallucinations and plausible-sounding post-hoc rationalisations
- Testing explainability via user verification speed and error-catching accuracy
Other Courses in This Category
-
Designing human-AI collaboration that amplifies human expertise: mixed-initiative workflows, preventing cognitive overload and skill atrophy, Microsoft HAX and Google PAIR guidelines, and co-creation loops.
- Live Course
- 5 modules
-
End-to-end metric architecture for AI products: North Star and task completion, acceptance rate and edit distance, offline/online eval correlation, retention and cost per successful task.
- Live Course
- 5 modules
-
Experimentation design for probabilistic AI products: moving from offline evals to online A/B tests, shadow deployments, interleaving, controlling for novelty effects and multi-metric ship criteria.
- Live Course
- 5 modules
-
Model behaviour and quality management for product teams: behaviour specifications (tone, boundaries, refusal style), systematic error analysis, domain-expert rubrics and continuous quality loops.
- Live Course
- 5 modules
-
Managing the quality, latency and cost triangle in AI products: model routing and cascading, prompt/semantic caching, token budgeting, perceived-speed UX and gross margin protection.
- Live Course
- 5 modules
-
Managing AI-native products where AI is the core engine rather than a bolted-on feature: compound AI systems, eval-driven development lifecycles, data flywheels and modern PM–AI engineer collaboration.
- Live Course
- 5 modules
Get in touch
