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A1· Architecture and planning

Computational & Generative Design

You turn design rules into Grasshopper definitions with an AI as your scripting partner, generate and rank options, and defend the one you choose.

Choose a planRead the syllabus
Who it is for
BArch students from third year up who have opened Rhino at least once. Rhino labs need a Windows or Mac laptop. This course builds computational skill; it does not replace the Council of Architecture's mandatory practical training in a registered architect's office.
AI core
9 units · 20 h
This course
6 modules · 12 lessons · 4.3 h
Live projects
2, on Professional and Career
01Roles it prepares you for
  • Computational Design Associate
  • Junior Architect
  • Design Technology Assistant

The skills these roles ask for. Preparation, not a promise of a job.

02Tools you will use
  • Rhino 8 (90-day evaluation or college lab licence)College licenceVerified
  • Grasshopper (ships inside Rhino)College licence
  • Autodesk Forma (Autodesk Education plan)College licence
  • Claude, ChatGPT or Gemini (free tier)Free tier
  • Saarthi sandbox (Saarthi coach and datasets)Saarthi sandbox

You never pay for a tool subscription. “Verified” means we checked the free access on the vendor’s own site this month. Tool names describe what you learn; no vendor endorses Launchpad.

03Syllabus

What you will learn, in order.

Lesson films are narrated with an AI voice, over visuals made with AI tools and checked by our team. Saarthi, the coach, is an AI. Mentors and reviewers are people.

Part 1 · The AI core, shared by every stream

00Set up your coach1 hLearn how the core works, write the learning-context document that tells every AI tool who you are, and set Saarthi up as a coach that makes you think instead of thinking for you.
  1. How this core works6 min
  2. Write your learning-context document8 min
  3. Seven roles for AI, and study modes7 min
01How LLMs work and fail2.5 hBuild a working mental model of a large language model: tokens, prediction, context windows and memory. Then learn its predictable failures (hallucination, sycophancy, stale knowledge, bad arithmetic) and reproduce them yourself.
  1. Tokens and next-word prediction7 min
  2. Context windows and memory7 min
  3. Hallucination, sycophancy and other failures8 min
  4. Lab: one prompt, three models, five failures8 min
02Description and context engineering3.5 hWrite briefs that get the right output the first time: role, task, context, examples and format. Then make them reusable with Claude and ChatGPT Projects, Gemini Gems, custom GPTs and NotebookLM, and learn to work with PDFs, spreadsheets and images.
  1. The brief: role, task, context, format8 min
  2. Examples and output formats7 min
  3. Projects in Claude and ChatGPT8 min
  4. Gems, custom GPTs and NotebookLM8 min
  5. Working with files: PDFs, spreadsheets, images8 min
03Verify everything2.5 hCheck AI output the way fact-checkers do: lateral reading, tracing citations and numbers to the original, using deep-research modes without trusting them blindly, and testing a prompt properly with a 10-case golden set.
  1. Lateral reading7 min
  2. Checking citations and numbers8 min
  3. Deep-research modes: use and limits7 min
  4. Your first eval: a 10-case golden set8 min
04Connectors and MCP2 hConnect AI to the places your work lives (Drive, Gmail, Sheets, business data) without writing code. Learn what an MCP server is, why a reviewed directory differs from unreviewed community servers, how to grant the least access that works, and how to keep browser extensions on a short leash.
  1. What connectors and MCP are8 min
  2. Trust, least privilege and the reviewed directory8 min
  3. Browser extensions and AI browsers7 min
  4. Lab: ask the seller-data MCP server, check every answer8 min
05Agents and automations3.5 hHand multi-step work to AI safely. Tell an agent from an automation, decide what to delegate, use browser and coding agents with care, and build an n8n workflow that uses AI with a human-approval step before anything leaves the system.
  1. Agents vs automations: what to delegate8 min
  2. Browser agents, safely7 min
  3. Automation basics with n8n and Zapier8 min
  4. Lab: build an automation with a human-approval step8 min
  5. Failure modes and your failure-mode note7 min
06AI for learning and careers1.5 hUse AI to learn faster without outsourcing the learning, write applications that still sound like you, and practise interviews out loud with Saarthi.
  1. Learning with AI, not instead of learning7 min
  2. Applications in your own voice7 min
  3. Mock interview with Saarthi8 min
07Diligence: law, ethics, integrity2.5 hThe rules that apply when you use AI in India: the DPDP Act 2023 and its 2025 Rules, academic integrity and disclosure, copyright (including where Indian law is still unsettled), and bias. You finish by writing your own AI-use policy.
  1. DPDP Act 2023: personal data and AI8 min
  2. Academic integrity and disclosure7 min
  3. Copyright and AI in India8 min
  4. Bias and fairness7 min
  5. Write your personal AI-use policy7 min
08Core capstone1 hPut all four Ds together on one anonymised marketplace settlement file: ask good questions, automate one step, verify every number against Saarthi's P&L engine, and defend your work live in five minutes.
  1. The capstone brief6 min
  2. Lab: ask, automate, verify8 min
  3. Prepare your 5-minute defence7 min

Part 2 · Computational & Generative Design

01Thinking in rules2 lessonsWhy computational design starts with a rule set, not a shape, and where AI fits.
  1. From drawing to rules14 min
  2. Your first definition with an AI pair25 min
02Scripting with an LLM2 lessonsGetting working RhinoCommon code out of a model, and catching the code that only looks right.
  1. Getting working code out of an LLM15 min
  2. Script an attractor facade panel30 min
03Generate and evaluate2 lessonsObjectives, constraints and solvers, and how to avoid optimising the wrong thing.
  1. Objectives, constraints and fitness15 min
  2. Run a solver on a massing30 min
04Bylaws as code2 lessonsReading development rules with an AI, turning them into checks, and keeping a human on every number.
  1. Turning bylaws into checks16 min
  2. Build a bylaw checker30 min
05Site analysis in Autodesk Forma2 lessonsFast early-stage environmental comparisons in a browser tool, and what they can and cannot tell you.
  1. Early-stage analysis in Forma14 min
  2. Compare three massings in Forma30 min
06Layout optimisation and defending your choice2 lessonsOptimising a warehouse layout for a seller, and presenting a generative study so a jury can audit it.
  1. Optimising a fulfilment layout16 min
  2. Defend your generative study25 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    Fulfilment-centre layout optimisation for a regional e-commerce seller

    A regional seller (synthetic, based on the Saarthi sandbox data pack) is moving into a rented 30 m x 45 m single-storey shed with a 7.5 m clear height, a 9 m column grid and two roller-shutter openings on one long side. It ships home and kitchen products, a few hundred orders a day, and uses hand pallet trucks only. Design the rack, aisle, dock and packing layout in Grasshopper, slot the products using an ABC analysis of the order lines, and minimise expected picker walking per order while meeting egress requirements you cite from NBC 2016 Part 4 and the local fire service. Compare at least three layouts generated from the same rules.

    You hand in

    • Grasshopper definition that generates layouts from sliders and reports walking distance, rack positions and validity
    • Verified rule sheet: equipment aisle widths from a data sheet, egress rules with clause references, assumptions in red
    • ABC analysis of the order lines, checked with a pivot table
    • Comparison of at least three layouts with walking distance per order and rack positions
    • Recommended layout drawing (plan with dimensions) and a one-page memo to the seller
    • AI-use log and disclosure statement

    Data: Saarthi sandbox: synthetic order lines (90 days, about 400 SKUs) and SKU dimensions for a home-and-kitchen seller. Shed geometry as given in the brief. No real seller data.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    Generative massing study under city bylaws

    For a real, publicly identifiable plot type in Mangaluru or Bengaluru (you choose a plot size and road width, not a specific owner's land), generate residential massing options that maximise usable built-up area and a daylight proxy while passing a bylaw checker built from the city's current zoning regulations and building bylaws. Every rule value must be extracted with an LLM and verified by you against the official document with clause and page. Present the design space, the trade-offs and your recommended option, and defend it in a live viva.

    You hand in

    • Verified rule CSV (minimum eight rules) with clause, page and verified_by for each row, and the official document link
    • Grasshopper definition with the bylaw checker, test cases and the generative solver
    • Design-space summary: options generated, valid count, failure reasons chart
    • Top options sheet plus one rejected high-scorer, and the recommended massing with area statement
    • Five-part study package (rule sheet, design space, results, decision, AI log)
    • Five-minute recorded or live defence

    Data: Public: the city's current zoning regulations and building bylaws (official PDF), plus open satellite imagery for context. Plot dimensions are chosen by the student; no private ownership data.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

05Practise out loud

In voice mock interviews and client calls, Saarthi plays: Kavya, design technology lead at a mid-sized Bengaluru practice. She opens your Grasshopper file during the call, asks why each slider has the range it has, and wants to hear which rule you would drop if the client cut the budget by a fifth.

On plans that include mock interviews. You get written feedback after every call.

06Enrol

Pick a plan for Computational & Generative Design.

One payment. The access deadline for each plan is shown before you pay.

  • Foundation

    01

    Learn the AI your field now runs on.

    ₹2,999

    One payment. GST included.

    • Access for 120 days (about 4 months) from the day you pay
    • No 1:1 mentor sessions
    • The common AI core and your domain course
    • Saarthi, your AI coach, any hour
    • Hands-on labs on free tools
    • Verifiable certificate
    Enrol on Foundation

  • Professional

    02

    Prove it on real work.

    ₹7,999

    One payment. GST included.

    • Access for 180 days (about 6 months) from the day you pay
    • One 1:1 session with a mentor
    • Everything in Foundation
    • Two live projects, reviewed by Saarthi and a mentor
    • Public portfolio page of verified work
    • Voice mock interviews
    • One 1:1 coaching session
    Enrol on Professional

  • Career

    03

    Work alongside our team.

    ₹14,999

    One payment. GST included.

    • Access for 240 days (about 8 months) from the day you pay
    • Four 1:1 sessions with a mentor
    • One 1:1 call with an industry expert
    • Everything in Professional
    • Four-week supervised practicum with the Saarthi team
    • Four 1:1 coaching sessions with your mentor
    • An exclusive 1:1 call with an industry expert in your field
    • CV and LinkedIn review
    • Priority mentor replies
    Enrol on Career

Your access period starts the day you pay and is shown before you pay. One exam pause per enrolment, for up to 15 days. When access ends the course locks, and your portfolio page and certificate stay live. Full refund within 7 days, pro-rata after that, and an automatic refund for any practicum, 1:1 mentor or expert session we do not deliver. Refund policy