Most AI prototyping tools generate polished UIs that look great but fall apart the moment engineering tries to build them. The problem isn’t the UI—it’s the lack of product context. lack of product context My workflow is to build a product‑expert AI agent first, generate a PRD inside it, break the experience into small flows, and then use contextual prompts to create prototypes that actually reflect real system behavior, data models, and constraints. product‑expert AI agent Many AI product managers use variations of this approach. What follows is the version I’ve refined and found most reliable for turning AI‑generated concepts into implementation‑ready prototypes. Why Traditional AI Prototype Workflows Break Down Most AI prototyping tools follow a simple pattern: Prompt → UI mockup Prompt → UI mockup It’s fast, but for anything beyond a simple consumer feature, it quickly fails. You get: Incorrect assumptions about system behavior
Missing or wrong data fields
Domain logic that doesn’t exist
Flows that engineering can’t implement
Pretty UIs with zero architectural grounding Incorrect assumptions about system behavior Missing or wrong data fields Domain logic that doesn’t exist Flows that engineering can’t implement Pretty UIs with zero architectural grounding The core issue: AI doesn’t know your product. Without grounding, it produces something imaginative, not buildable. AI doesn’t know your product The Shift: Context‑Driven AI Prototyping Instead of starting in a prototyping tool, I flipped the workflow: Build context → Generate a PRD → Define flows → Produce contextual prompts → Generate prototype Build context → Generate a PRD → Define flows → Produce contextual prompts → Generate prototype The result is a prototype aligned with how the system actually works—not how a model guesses it might work. 1. Build a Product‑Expert AI Agent Before touching UI, I create an AI agent that deeply understands the product. You can use ChatGPT, Microsoft Copilot, or any agent framework that you prefer. I load it with: Help docs
Strategy decks & roadmaps
Release notes
API references
Developer guides
One‑pagers / internal overviews
Any content I can consolidate into structured PDFs Help docs Strategy decks & roadmaps Release notes API references Developer guides One‑pagers / internal overviews Any content I can consolidate into structured PDFs This single step changes everything. Once trained, the agent knows: Core concepts & data models
Domain rules & constraints
Historical decisions
System behaviors
Edge cases
Terminology & internal language Core concepts & data models Domain rules & constraints Historical decisions System behaviors Edge cases Terminology & internal language Now I can ask things like: “Write a PRD for X.”
“Generate API samples for this workflow.”
“List all user actions in this system.”
“What assumptions am I missing?” “Write a PRD for X.” “Write a PRD for X.” “Generate API samples for this workflow.” “Generate API samples for this workflow.” “List all user actions in this system.” “List all user actions in this system.” “What assumptions am I missing?” “What assumptions am I missing?” The output becomes significantly more accurate than a generic LLM conversation. 2. Generate and Workshop the PRD Inside the Agent Next, I use the agent to create a PRD for feature X. PRD for feature X But this PRD is not the final version—it’s a starting point. I review and workshop it for any incorrect assumptions, missing fields or behaviors, product and domain gaps or architectural conflicts. I iterate inside the agent until the PRD reflects the real system. This step increases time to the first prototype, but it dramatically accelerates everything after—especially cross‑functional conversations. dramatically accelerates everything after 3. Break the Experience Into Small, Independent Flows AI struggles with large, complex requests. So I break the experience into discrete flows, such as (this may be different for your product): Landing page
Create Report
View insights
Manage Subscriptions
View and Download Landing page Create Report View insights Manage Subscriptions View and Download Small context → Higher accuracy → Less rework Small context → Higher accuracy → Less rework Each flow becomes its own micro‑iteration of project. 4. Have the Agent Generate the Prototype Prompt Now that the agent understands the PRD and flows, I ask: “Generate the exact prompt to produce a prototype for this flow.” “Generate the exact prompt to produce a prototype for this flow.” The agent produces a prompt like: ✅ Prototype Objective ✅ Prototype Objective Design a user interface that enables users to: Design a user interface that enables users to: Create, customize, and schedule reports
View and download generated reports
Manage report subscriptions
Filter and organize data by merchant, product, and transaction attributes Create, customize, and schedule reports Create, customize, and schedule reports View and download generated reports View and download generated reports Manage report subscriptions Manage report subscriptions Filter and organize data by merchant, product, and transaction attributes Filter and organize data by merchant, product, and transaction attributes 🧩 Step-by-Step Instructions for VIBE Prototype 🧩 Step-by-Step Instructions for VIBE Prototype 1. Landing Page – “Reporting Dashboard” 1. Landing Page – “Reporting Dashboard” Components:

Navigation panel (left): Downloadable Reports, Available Reports, Report Subscription Management
Summary cards: Total reports generated, active subscriptions, average generation time
CTA buttons: Create Report, View Subscriptions, Download Reports Components:

Navigation panel (left): Downloadable Reports, Available Reports, Report Subscription Management
Summary cards: Total reports generated, active subscriptions, average generation time
CTA buttons: Create Report, View Subscriptions, Download Reports Components: Navigation panel (left): Downloadable Reports, Available Reports, Report Subscription Management
Summary cards: Total reports generated, active subscriptions, average generation time
CTA buttons: Create Report, View Subscriptions, Download Reports Navigation panel (left): Downloadable Reports, Available Reports, Report Subscription Management Navigation panel (left): Downloadable Reports, Available Reports, Report Subscription Management Summary cards: Total reports generated, active subscriptions, average generation time Summary cards: Total reports generated, active subscriptions, average generation time CTA buttons: Create Report, View Subscriptions, Download Reports CTA buttons: Create Report, View Subscriptions, Download Reports 2. Report Creation Flow – “Create Report” Modal/Page 2. Report Creation Flow – “Create Report” Modal/Page Trigger: Click Create Report from Dashboard or Available Reports
Sections:

Account Setup (for partner users):

Dropdown: Select Merchant or Group


Basic Report Setup:

Text input: Report Name (max 250 characters)
Dropdown: Report Type (e.g., Transaction Request, Payment Batch Summary)
Dropdown: File Format (CSV or XML)
Dropdown: Frequency (One-time, Daily, Weekly, Monthly)


Advanced Report Features (expandable panel):

Toggles: Credit Amounts as negative, Naming Convention (camelCase or snake_case)
Multi-select filters:

Application (e.g., Sales, Refunds)
Connection Method (e.g., API, Terminal)
Payment Channel (e.g., VisaNet, Mastercard)


Field Selection:

Searchable list of fields
Checkboxes to include/exclude fields
Drag-and-drop ordering (CSV only)






Actions:

Button: Create → triggers report generation or subscription setup Trigger: Click Create Report from Dashboard or Available Reports Trigger: Click Create Report from Dashboard or Available Reports Sections:

Account Setup (for partner users):

Dropdown: Select Merchant or Group


Basic Report Setup:

Text input: Report Name (max 250 characters)
Dropdown: Report Type (e.g., Transaction Request, Payment Batch Summary)
Dropdown: File Format (CSV or XML)
Dropdown: Frequency (One-time, Daily, Weekly, Monthly)


Advanced Report Features (expandable panel):

Toggles: Credit Amounts as negative, Naming Convention (camelCase or snake_case)
Multi-select filters:

Application (e.g., Sales, Refunds)
Connection Method (e.g., API, Terminal)
Payment Channel (e.g., VisaNet, Mastercard)


Field Selection:

Searchable list of fields
Checkboxes to include/exclude fields
Drag-and-drop ordering (CSV only) Sections: Account Setup (for partner users):

Dropdown: Select Merchant or Group


Basic Report Setup:

Text input: Report Name (max 250 characters)
Dropdown: Report Type (e.g., Transaction Request, Payment Batch Summary)
Dropdown: File Format (CSV or XML)
Dropdown: Frequency (One-time, Daily, Weekly, Monthly)


Advanced Report Features (expandable panel):

Toggles: Credit Amounts as negative, Naming Convention (camelCase or snake_case)
Multi-select filters:

Application (e.g., Sales, Refunds)
Connection Method (e.g., API, Terminal)
Payment Channel (e.g., VisaNet, Mastercard)


Field Selection:

Searchable list of fields
Checkboxes to include/exclude fields
Drag-and-drop ordering (CSV only) Account Setup (for partner users):

Dropdown: Select Merchant or Group Account Setup (for partner users): Dropdown: Select Merchant or Group Dropdown: Select Merchant or Group Dropdown: Select Merchant or Group Basic Report Setup:

Text input: Report Name (max 250 characters)
Dropdown: Report Type (e.g., Transaction Request, Payment Batch Summary)
Dropdown: File Format (CSV or XML)
Dropdown: Frequency (One-time, Daily, Weekly, Monthly) Basic Report Setup: Text input: Report Name (max 250 characters)
Dropdown: Report Type (e.g., Transaction Request, Payment Batch Summary)
Dropdown: File Format (CSV or XML)
Dropdown: Frequency (One-time, Daily, Weekly, Monthly) Text input: Report Name (max 250 characters) Text input: Report Name (max 250 characters) Dropdown: Report Type (e.g., Transaction Request, Payment Batch Summary) Dropdown: Report Type (e.g., Transaction Request, Payment Batch Summary) Dropdown: File Format (CSV or XML) Dropdown: File Format (CSV or XML) Dropdown: Frequency (One-time, Daily, Weekly, Monthly) Dropdown: Frequency (One-time, Daily, Weekly, Monthly) Advanced Report Features (expandable panel):

Toggles: Credit Amounts as negative, Naming Convention (camelCase or snake_case)
Multi-select filters:

Application (e.g., Sales, Refunds)
Connection Method (e.g., API, Terminal)
Payment Channel (e.g., VisaNet, Mastercard)


Field Selection:

Searchable list of fields
Checkboxes to include/exclude fields
Drag-and-drop ordering (CSV only) Advanced Report Features (expandable panel): Toggles: Credit Amounts as negative, Naming Convention (camelCase or snake_case)
Multi-select filters:

Application (e.g., Sales, Refunds)
Connection Method (e.g., API, Terminal)
Payment Channel (e.g., VisaNet, Mastercard)


Field Selection:

Searchable list of fields
Checkboxes to include/exclude fields
Drag-and-drop ordering (CSV only) Toggles: Credit Amounts as negative, Naming Convention (camelCase or snake_case) Toggles: Credit Amounts as negative, Naming Convention (camelCase or snake_case) Multi-select filters:

Application (e.g., Sales, Refunds)
Connection Method (e.g., API, Terminal)
Payment Channel (e.g., VisaNet, Mastercard) Multi-select filters: Application (e.g., Sales, Refunds)
Connection Method (e.g., API, Terminal)
Payment Channel (e.g., VisaNet, Mastercard) Application (e.g., Sales, Refunds) Application (e.g., Sales, Refunds) Connection Method (e.g., API, Terminal) Connection Method (e.g., API, Terminal) Payment Channel (e.g., VisaNet, Mastercard) Payment Channel (e.g., VisaNet, Mastercard) Field Selection:

Searchable list of fields
Checkboxes to include/exclude fields
Drag-and-drop ordering (CSV only) Field Selection: Searchable list of fields
Checkboxes to include/exclude fields
Drag-and-drop ordering (CSV only) Searchable list of fields Searchable list of fields Checkboxes to include/exclude fields Checkboxes to include/exclude fields Drag-and-drop ordering (CSV only) Drag-and-drop ordering (CSV only) Actions:

Button: Create → triggers report generation or subscription setup Actions: Button: Create → triggers report generation or subscription setup Button: Create → triggers report generation or subscription setup Button: Create → triggers report generation or subscription setup A prompt like this would take hours to fine tune manually— but the agent can do it in seconds with full context. 5. Create the Prototype Using a Contextual Prompt Now I finally open an AI prototyping tool. But instead of: “Build a UI for batch uploads…” I give it a fully contextualized prompt with domain rules, data structures, field definitions, behaviors, API inputs/responses, error paths, interaction expectations. fully contextualized prompt This produces something radically closer to implementation reality. The UI still needs polish, but the logic, fields, and flows are correct, which is what matters for engineering alignment. logic, fields, and flows are correct 6. Validate Early With Design & Engineering The prototype is not “final UI,” but it’s far enough along that Designers avoid starting from a blank screen and Engineers can validate feasibility immediately. It becomes a shared artifact for meaningful cross‑functional discussion, not a theoretical mockup. shared artifact Benefits of Context‑Driven Prototyping I have observed that this workflow consistently delivers: Prototypes that reflect real behavior, not hallucinated logic
Faster alignment between product, design, and engineering
Reduced ambiguity in requirements
Less rework in later stages
Better architectural grounding
Earlier discovery of constraints and dependencies Prototypes that reflect real behavior, not hallucinated logic Faster alignment between product, design, and engineering Reduced ambiguity in requirements Less rework in later stages Better architectural grounding Earlier discovery of constraints and dependencies For complex systems, this is a game‑changer. Risks & Tradeoffs Context‑driven prototyping is powerful, but not perfect. Longer ‘time to first prototype’.
Agent drift or bias - Different models can interpret context differently.
The agent must be updated constantly after new releases, APIs, or architectural changes.
UI stills need design refinement.
If you’re blue‑sky brainstorming, this approach may be unnecessarily heavy. Longer ‘time to first prototype’. Agent drift or bias - Different models can interpret context differently. The agent must be updated constantly after new releases, APIs, or architectural changes. UI stills need design refinement. If you’re blue‑sky brainstorming, this approach may be unnecessarily heavy. Closing Thoughts Context‑driven prototyping has completely changed how I build with AI. Instead of relying on generic, surface‑level prompts, I anchor every prototype in real system knowledge, which dramatically improves accuracy and reduces alignment friction. As AI tools evolve, context-first > prompt-first will be the difference between prototypes that look impressive… and prototypes that engineering teams can actually build. context-first > prompt-first

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

Why Most AI Prototypes Fail When Engineering Gets Involved

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