Learn Cursor as a serious AI development environment — not as autocomplete, not as a vibe-code tool.
Cursor gives you a powerful chat surface, project rules, MCP integration, and an editor that sees your codebase. The curriculum teaches you to use those surfaces inside Ananth Godavari’s Builder Lab Method — so the AI participates in real software delivery, not just code generation.
The same eight-pattern curriculum as the canonical hub, taught specifically through Cursor. Method stays portable to Claude Code, Antigravity, GitHub Copilot — see the portability panel below.
A powerful chat surface, a project rules mechanism, MCP integration, an editor that sees your codebase, and a conversation that persists across sessions.
What rules to design against what failure modes; what artifacts the project needs; what runtime evidence the AI must consume to verify itself; when “done” is actually done.
Who this Cursor curriculum serves
- auto_awesomeAI tool users
- codeWorking developers
- schoolStudents
- trending_upCareer changers
- buildSelf-taught builders
What you’ll leave able to do in Cursor
Eight capabilities — framed for Cursor.
The same eight pattern-named outcomes the canonical curriculum trains for, taught specifically through Cursor’s rules, chat, MCP, and codebase-awareness surfaces. The discipline is what makes AI tools reliable inside real software delivery; Cursor is where it runs.
Multi-tier artifact hierarchy — Cursor consults it
Build the project hierarchy Cursor reads as authoritative context before contributing — rules, briefs, decisions, conversation notes, all cross-linked. Decisions stop dying in chat.
Cursor project rules as failure-mode instruments
Each rule in .cursor/rules/*.mdc exists because you can name the specific failure it prevents. Rules become guards, not generic style guides.
Diagnostic agents Cursor reads in the next chat turn
Build state-dump agents whose output Cursor consumes in the next chat turn — so Cursor verifies its own work against ground truth, not against assumptions. The developer arbitrates.
Operator-in-the-loop ledger inside Cursor’s chat
Run the deployment record inside Cursor’s chat — the conversation that produces the deployment becomes the audit record. Future Cursor sessions can read it.
Per-session Cursor work logs keyed by machine + author
Maintain Cursor session logs per work-unit. Your future self can read across logs and see how you actually used the AI — spot AI-discipline drift before it becomes technical debt.
Meeting transcripts as Cursor-consumable upstream context
Convert verbal decisions into Cursor-readable artifacts so verbal decisions inform code work for the life of the project, not just the conversation they happened in.
Portable rules pack & discipline-first scaffolding
Initiate new Cursor projects with a portable rules pack and project-template artifacts before any feature ships. Discipline established at t=0; the method evolves through your portfolio.
Direct Cursor to build its own verification scaffolding
Use Cursor under your direction to build the project-specific verification scaffolding the AI then operates inside. Closes the loop on AI-built software.
Where the method lives in Cursor
Method pattern → Cursor surface.
The eight patterns map to specific places in the Cursor UI. Knowing where to look — for what — is a separate skill from knowing the method itself. The curriculum teaches both.
Pattern: failure-mode rules
Cursor surface
.cursor/rules/*.mdc files in the project root — one rule file per failure mode you can name. Cursor auto-attaches these as context; you don’t paste them.
What you do here: author each rule against a specific failure the project would otherwise produce. Front-matter governs scoping.
Pattern: artifact hierarchy
Cursor surface
AGENTS.md at the repo root + a /docs/ tree of decisions, briefs, and ADRs — all referenced from rules so Cursor reads them as authoritative context.
What you do here: design the layered hierarchy your rules point Cursor at on every relevant turn. This is the project memory.
Pattern: runtime evidence loop
Cursor surface
Diagnostic agents you build (typical home: /tools/ or /scripts/); their dump output added to chat as a file reference so Cursor reads it as ground truth.
What you do here: ask Cursor to verify against the dump, not against assumptions. The chat turn is the evidence loop.
Pattern: operator-in-chat ledger
Cursor surface
Cursor’s chat panel itself — the conversation that produces a deployment becomes the audit record of the deployment. Sub-step lifecycle conventions govern when a step closes.
What you do here: type your operator confirmations in chat; reference them in the next session via stable identifiers.
Pattern: session logs
Cursor surface
Per-work-unit log files you write into the project (typical home: /docs/sessions/) keyed by machine + author. Cursor’s built-in agent transcripts complement — they don’t replace.
What you do here: close each work-unit by writing the log; future sessions read across logs as architectural evidence.
Pattern: transcripts as upstream context
Cursor surface
Meeting/scrum transcripts you ingest into the project (typical home: /docs/transcripts/); rules and ADRs reference them so Cursor pulls them in as context on related work.
What you do here: turn verbal decisions into Cursor-readable artifacts; reference them from the rules that govern the affected files.
Prompts — even with rules in place
Four prompt categories that still matter.
Project rules establish the floor: how Cursor must operate against this codebase. Prompts are how you drive Cursor above the floor toward the specific piece of work in front of you. Four categories worth knowing — the rationale is public; the specific templates are part of the curriculum.
Category 1
Scoping prompts
Direct Cursor’s attention to the specific files, ADRs, and rules that govern the work in front of you — without flooding context with the entire repo. The rules tell Cursor how to think about the codebase; scoping prompts tell it where to look.
Why it still matters: rules are uniformly attached, but the work in front of you is not uniform. A bad scope produces a confident answer about the wrong code.
lock Specific scoping templates & exercises in the curriculum bundle.
Category 2
Failure-naming prompts
Ask Cursor to name what could go wrong before it generates code. Surfaces the failure modes the rules don’t yet cover — which then become candidate new rules in the project’s rules pack.
Why it still matters: rules encode known failure modes. Failure-naming prompts surface the unknown ones; the method’s rule pack grows from these surfaces.
lock Failure-naming prompt templates & rule-promotion exercise in the curriculum bundle.
Category 3
Evidence-grounded prompts
Ask Cursor to verify against the runtime artifacts the rules already point at — the diagnostic agent dumps, the baselines, the session logs — not against assumptions or its own prior turn. The chat turn becomes the evidence loop.
Why it still matters: Cursor will confidently fabricate the state of code or data unless explicitly directed at runtime evidence. Evidence-grounded prompting closes that gap.
lock Evidence-grounding templates wired to the diagnostic-agent pattern in the curriculum bundle.
Category 4
Reversal prompts
Instruct rollback through the rules-aware ledger when a sub-step needs to be reversed — so the reversal is recorded as a first-class event the project’s decision log captures, not silently re-edited away.
Why it still matters: reversed decisions are architectural evidence. A reversal that vanishes from the record costs the next architect the lesson it taught.
lock Reversal-prompt templates & ledger-update conventions in the curriculum bundle.
Tools used in the curriculum
The curriculum’s discipline runs in any AI development environment with persistent project rules and an in-IDE conversation surface. Cursor is the primary teaching vehicle; the method itself adapts to other AI development environments.
Cursor
Primary teaching vehicle
The AI-first IDE used across the working examples this curriculum is taught from.
Claude Code
Adaptable host
The artifact-and-ritual discipline transfers cleanly when teams standardize on Claude Code.
Antigravity
Adaptable host
Same project-rules + in-IDE conversation pattern; same artifact hierarchy travels.
GitHub Copilot
Adaptable host
Per-repo guidance and chat surface meet the requirements; the method runs at team scale here too.
info Chat-only AI use without an IDE-integrated rules surface is out of scope for the method.
Curriculum — Cursor edition
Eight modules. Same method. Cursor-specific examples throughout.
The curriculum below is the canonical eight-module sequence from /ai-builder-lab-training, taught here entirely through Cursor. Module names link into the canonical hub for full objectives and depth.
Capstone — Cursor edition
Run the full method on one of two named capstone projects.
The capstone is the vehicle, not the trophy. Both projects are deliberately small in scope — focused enough to absorb the eight patterns at once, real enough to integrate vision-AI as a product feature. Both train two layers of AI engagement at the same time: AI inside the build (Cursor as the AI co-developer) and AI integrated as the product feature.
Full module objectives, sequence, and the canonical capstone framing live on /ai-builder-lab-training.
What you take with you — Cursor edition
Two deliverables. Both Cursor-native.
The capstone is the vehicle for practicing the method. What you take with you is two artifacts: the documented project bundle you produce during the capstone (it lives in the Cursor project repo), and the latest Builder Lab rules pack in Cursor-native format ready to drop into your next .cursor/rules/ tree.
What your Cursor capstone produces
A documented project bundle living in the Cursor project repo.
A learner finishing the engagement-shaped path produces the artifact set below alongside the working code — everything Cursor can read on the next session, including the next architect’s. The artifact names are public; the operational templates and exercises live in the engagement.
.cursor/rules so Cursor pulls them into related turns.info Self-paced and audited paths produce a reduced artifact set; the engagement-shaped path is the only one that produces the full set above.
Plus, you take this with you
The latest Builder Lab rules pack — yours to keep.
Beyond the working knowledge you gain in the curriculum, every student receives the current latest version of the Builder Lab rules pack on completion — so the next project you start already has the discipline-first scaffolding the method runs on.
What’s in the pack
Rules & project-template artifacts
- check Multi-tier artifact hierarchy conventions
- check Failure-mode AI guardrail patterns
- check Decision log & ADR INDEX templates
- check Retirement archive header pattern
- check Per-session work log scaffolding (machine + author keyed)
- check Sub-step lifecycle conventions (operator-confirms-in-chat)
- check Sign-off register conventions (deploy ledger in chat)
- check Baseline file scaffolding & project-init README
Format & portability
Cursor-native, with translation notes
- chat
Ships as
.cursor/rules/*.mdcfiles — drop into any Cursor project - terminal Translation guidance for Claude Code, Antigravity, GitHub Copilot
- folder_open Project-template artifacts as plain Markdown — framework-agnostic
Version & usage
Latest at completion, yours to apply
- update You receive the current latest version at the time you complete the course
- key Yours to apply on any project you work on after the course
- menu_book
Ships with a usage README and
LICENSE(MIT License) — see the file for redistribution terms
info The pack’s rule contents and project-template files are delivered to course participants. The categories above are public; the templates themselves stay with the course.
The README notes Builder Lab provenance for your records. There is no separate “credits in your build pipeline” requirement beyond honoring the shipped LICENSE when you distribute copies of the kit.
Method portability
Method portability
Cursor is the teaching vehicle. The method travels with you.
This curriculum teaches the Builder Lab Method through Cursor. The method itself is tool-agnostic — students who later adopt Claude Code, Antigravity, GitHub Copilot, or another AI development environment with persistent project rules and an in-IDE conversation surface can carry the same artifact-and-ritual discipline. Cursor is the teaching vehicle; the method is the deliverable.
Cursor (taught here)
Project rules in .cursor/rules/*.mdc, in-IDE chat as conversation-of-record, MCP integration, codebase awareness, persistent context across sessions.
Adaptable to
Claude Code, Antigravity, GitHub Copilot, and other AI development environments that support persistent project rules and an in-IDE conversation surface. Same artifact hierarchy, same ritual discipline.
Out of scope
Chat-only AI use without an IDE-integrated rules surface (e.g., a chat tab pasted into a separate editor). The method requires the AI to operate inside the project’s artifact-and-ritual machinery; a chat-only surface cannot enforce that.
infoThe rule pack ships in Cursor-native format with translation guidance for the other AI dev environments — so a switch later doesn’t mean starting over.
Ready to learn Cursor as an AI development environment?
Use Cursor inside the discipline that makes AI tools reliable.
Eight modules. Two named capstone projects. The documented project bundle, and the latest rules pack to take into your next Cursor project. Method portable to Claude Code, Antigravity, GitHub Copilot.
Or open a conversation about training directly — no enrollment form, no commitment.