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Most AI products fail not because the model is wrong - but because the design never accounted for what happens when the model is uncertain, partially correct, or wrong entirely. This course teaches you to design every state: confident output, uncertain output, graceful failure, and everything in between.
Last updated: 9 June 2025
67 hrs
Total
12
Students Per Cohort
2
Portfolio Case Studies
5
AI Tools Taught Live
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AI Product Design Course
2
Portfolio case studies
8
AI states designed
1
Functional AI prototype
Module 1
Module 2
Every artefact below is shipped during the course - not after it.
Live teardown of 3 real AI products. Probabilistic vs deterministic systems. Where trust collapses and what the design missed.
Tools: Perplexity, Claude
Deliverable: AI product teardown doc
The 5-criteria trust audit. Students choose the live AI product they will audit across the course. Perplexity research sprint. Claude synthesises audit brief.
Tools: Perplexity, Claude
Deliverable: Structured audit brief
What an AI behaviour specification is. Defining all AI states before opening Figma. Content design for AI voice across states. Live Claude workshop: writing behaviour specs and iterating on AI state copy.
Tools: Claude
Deliverable: AI behaviour specification
Decision trees for probabilistic systems. Human-in-the-loop design points. Trust architecture diagrams built live.
Tools: Figma
Deliverable: Trust architecture diagram and AI decision tree
The 8 core AI states: loading and thinking, generating, confident output, uncertain output, partial output, error, refusal, empty. Confidence UI patterns. AI state component library built in Figma from scratch.
Tools: Figma, Claude
Deliverable: Figma AI state library - all 8 states
Hallucination handling UI. The "I don't know" experience as a product feature. Feedback loop design. Live redesign of identified trust failures in the audit product.
Tools: Figma
Deliverable: Audit product redesigned - minimum 5 screens
Choosing an original AI feature. Writing the full behaviour spec in Claude. Prompt chain workshop.
Tools: Claude
Deliverable: Original AI feature behaviour spec
Applying the Week 3 state library to the original feature. IA for AI-native features. Trust scaffolding design. Full design sprint.
Tools: Figma
Deliverable: Original feature - minimum 12 screens across all states
Why Figma prototypes fail for AI features. Prompt engineering for Bolt and v0. Building the working prototype live.
Tools: Bolt or v0.dev, Claude
Deliverable: Working interactive prototype
Claude-simulated user responses across trust states. Identifying where the prototype breaks trust. Iteration sprint. Prompt chain documentation complete.
Tools: Claude, Bolt or v0.dev
Deliverable: Iterated prototype, usability findings, prompt chain documentation
Audit case study. Original feature case study. Claude for writing, editing, and signal-to-noise ratio.
Tools: Claude
Deliverable: Both case studies written
Live presentations to full cohort. Structured critique. Checklist review. Certificate issued.
Deliverable: Complete portfolio - 2 case studies, Figma files, prototype, prompt chain, certificate
By end of week 3, every student has a Figma component library with all 8 AI states built. Production-ready. Built on a real product problem.
Most AI products design only states 1 and 3. This course designs all 8.
Weeks 1 and 2. AI product audit research. Competitive benchmarking, feature pattern analysis, user complaint synthesis.
Every week. Behaviour spec writing, AI state copy, prompt chain construction, simulated usability testing, case study writing. The most heavily used tool in this course.
Weeks 2 through 5. Trust architecture diagrams, AI decision trees, human-in-the-loop flow mapping, all 8 AI states, component library, audit product redesign, original feature design across all states.
Weeks 5 and 6. Functional interactive prototypes where AI states are actually interactive - not static Figma click-throughs.
AI Track progression - where this specialisation sits.
AI Product Design Course
← You are here
Pro graduates receive … off. Open to UI UX Design Pro graduates and experienced PMs.
After this course, consider UI UX Design Master for the full senior design curriculum in parallel.
FREE ENTRY POINT
Try the free AI Product Design Live workshop first.
If you want to learn how to use ChatGPT to generate wireframes or Figma Make to speed up UI - that is not what this is.
This course is about something harder and more valuable: designing products where AI is the core behaviour. Where the output is probabilistic. Where the user needs to trust a system that is sometimes wrong, sometimes uncertain, and occasionally refuses to answer.
That requires a different skill set entirely - one that no course in India currently teaches at this depth.
8
Advantages vs generic courses
8
AI states in Figma
Core focus
Designing AI behaviour - all states
Using AI tools to design faster
Portfolio output
2 case studies + working prototype
Concepts and certificates
AI states covered
All 8 - including error, fallback, refusal
Happy path only
Format
Live, application-based, 12 students
Recorded or large cohort
| Feature | This Course | Generic AI + Design Courses |
|---|---|---|
| Core focus | … | Using AI tools to design faster |
| Portfolio output | … | Concepts and certificates |
| AI states covered | … | Happy path only |
| Format | … | Recorded or large cohort |
| Prototype format | … | Static Figma prototype |
| Entry bar | … | Open to anyone |
| Batch size | … | 50 to 200+ |
| Toolchain | … | Figma only |
This is the question a thoughtful buyer will ask. Here is the direct answer.
1. Half the batch size.
12 students vs 25 in Pro. The critique sessions, mentor time, and feedback quality per student are structurally different at 12 seats. That ceiling cannot be maintained at Pro pricing.
2. Application-based enrollment.
Every student in this cohort has shipped a digital product. The cohort quality - the calibre of questions, the brief complexity, the peer critique depth - is significantly higher than an open-enrollment course. You are not paying for access. You are paying for the cohort you are entering.
3. A more experienced, more expensive mentoring profile.
The mentors who can credibly teach AI behaviour design - trust calibration, probabilistic UX, LLM feature specification - are senior product practitioners, not junior design instructors. The teaching cost reflects that.
4. A specialist discipline with almost no competition at depth in India.
A handful of programs abroad cover this at comparable depth - at USD 1,500 to USD 2,000, self-paced, no live critique, no portfolio output. At Rs 64,999 with live mentorship and two deployable case studies, the price is structurally lower than every international equivalent.
5. Two portfolio artefacts that cannot be produced in a general design course.
An AI product audit case study and a functional Bolt/v0 prototype of an original AI feature are specialist outputs. The time, mentorship, and tool access required to produce them is not comparable to a general UI UX curriculum.
The Pro course builds a design foundation. This course builds a specialisation on top of it. They are not competing - they are sequential for designers, and parallel for PMs who come from a product background rather than a design background. UI UX Design Pro is the foundation course this specialisation builds on.
Notion template - all 8 AI states with pre-built structure, example copy, and a checklist. Use from week 1 and on every AI project after.
Standalone value: … · Included
Figma file - all 8 AI states pre-built as components. The structure you build in week 3, given as a reference in week 1.
Standalone value: … · Included
50 tested prompts for Bolt and v0.dev - AI state simulation, interactive transitions, confidence indicator rendering, fallback state prototyping.
Standalone value: … · Included
2-hour async module covering exactly the Figma operations this course requires. Built for PMs and non-designers. Released the week before the cohort starts.
Standalone value: … · Included
Direct access to your mentor via the cohort channel for 60 days after the final session.
Standalone value: … · Included
Dedicated end-of-course session - structured critique of both case studies using a defined feedback rubric.
Standalone value: … · Included
Permanent access to the alumni channel - peer critique, job leads, project collaborations.
Standalone value: … · Included
Redeemable in weeks 3 to 5. Use it on your Figma state library, original feature design, or prototype.
Standalone value: … · Included
You save ~82% vs buying everything separately
AI Product Design Course (6 weeks · 42 live hours · 2 case studies · prototype)
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AI Behaviour Spec Template Library
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AI State Design System Kit
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AI Prototype Prompt Vault
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Figma for Non-Designers Pre-Course Module
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60-Day Post-Course Mentor Access
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Live Group Portfolio Review Session
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AI Product Design Alumni Community
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[Early Bird] 1:1 Mentor Session
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Application-based. 12 seats. Two portfolio artefacts in 6 weeks.
Limited seats per batch. To maintain quality mentorship and personalised feedback, each batch is intentionally kept small. Admissions close once seats are filled.
Accepted students pay a Rs 6,499 booking token to confirm their seat. Non-refundable. Adjusts against full fee.
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Book a free demo callFill out the form below to submit your application for review.
Questions? Contact us or view full pricing details.
If you attend every session, submit every deliverable on time, and still do not have a portfolio-quality audit case study and functional prototype at course end - you receive a 25% refund.
Written claim within 14 days of the final session. Students who miss sessions or skip deliverables are ineligible. Full terms at /cancellation-refund-policy.
The AI Product Design Course is for product people who have shipped digital work and want to specialise in designing AI-native products | not for beginners learning design from scratch.
This program is for people who are serious about building a design career, not just collecting course badges.
Common questions about the bootcamp, curriculum, and enrollment process.