AI Bot
Prototype with
Claude Code
AI Bot
Prototype with
Claude Code
From Design to AI Prototype in 3 Days: Building a Conversational AI Bot with Claude Code
From Design to AI Prototype in 3 Days: Building a Conversational AI Bot with Claude Code
Embedded Files
Prototype with
Claude Code
SUMMARY
In 2 weeks, our team had to validate a complex Conversational AI concept before committing to a major engineering roadmap. Multiple stakeholders had different visions. Rather than spend months debating, I built a fully functional AI prototype in 3 days using Claude Code—proving the concept, aligning the executive team, and establishing a new working model for rapid AI validation at the company.
KEY INSIGHTS: Strategic planning + AI-assisted building = fast, aligned, validated prototypes. But only if you have the right plan first.
CONTEXT: THE PRESSURE
The Situation
The Situation
We were designing Conversational AI features for SugarCRM's sales platform. The vision was clear in design—but the execution path was murky.
We were designing Conversational AI features for SugarCRM's sales platform. The vision was clear in design—but the execution path was murky.
WE HAD:
✅ Figma designs showing the seller experience
✅ Research insights on what sellers needed
❌ No working prototype to align stakeholders
❌ Engineering ready to commit, but uncertain about feasibility
❌ Multiple executive visions on what "Conversational AI" should actually do
The Real Problem
The Real Problem
It wasn't about building code.
It was about alignment under time pressure.
It wasn't about building code.
It was about alignment under time pressure.
→ Executives had different mental models of how this AI should work.
→ Sales leaders wanted proof it would actually help sellers.
→ Product managers questioned whether Claude's capabilities were sufficient for our use case.
→ Engineering wanted clear requirements before investing months of work.
CHALLENGE:
I could have taken the obvious path: spend weeks writing detailed product specs, hand off to engineering, wait months for a build.
But that didn't fit the timeline or solve the alignment problem. Instead, I decided to build a working prototype in 3 days using Claude Code.
THE INITIAL MISTAKE
Speed
Without Strategy
Speed
Without Strategy
Claude made things I didn’t wanted and spent my daily token limit.
Day 1
Started WrongDay 1
I jumped straight into Claude Code with the mindset: "I'll just prompt my way to a prototype." I had my Figma designs. I had a general vision. I started building.
0%
Results0%
Token hemorrhage. Confused outputs. The bot responses weren't what I wanted. The conversation flows were clunky. I was iterating blindly, spending tokens on endless refinements, and getting further from the goal with each prompt.
100%
The core issue100%
I had no plan.
I was hoping Claude would figure it out. This is where I learned the first critical lesson: Garbage in, garbage out. Without a proper plan, you'll spend resources chasing half-baked ideas.
THE SOLUTION
Day 2
Planning First → Building SecondDay 2
Step 1:
I stopped
building.
I stopped
building.
On Day 2, I changed my approach entirely.
First I educated my self and installed proper Claude Skills. Then, I used Claude Plan mode to think through the architecture before writing code.
Step 2:
I described
the full vision.
I described
the full vision.
"I have Figma designs showing a Conversational AI. And I need your help to make a plan of how to create a prototype that will match Figma design.
I will send you later the figma link."
Step 3:
I shared my
Figma designs.
I shared my
Figma designs.
I connected Figma to Claude. Then I shared Figma Designs and Claude's design context capabilities, I let Claude analyze the actual UI/UX I'd designed.
Step 4:
I asked
clarifying questions.
I asked
clarifying questions.
"How should the bot understand user's context? What information does it need? How should responses be structured?"
Step 5:
I let Claude
ask me questions.
I let Claude
ask me questions.
Claude prompted me with gaps I hadn't considered:
How should the bot handle edge cases?
What's the scope of information it should access?
How do we balance automation with seller judgment?
Day-by-Day
Breakdown
Day-by-Day
Breakdown
The prototype
consisted of:
The prototype
consisted of:
1. Conversational Engine - Prompt engineering structured around the seller's context (account, opportunity, meeting type)
2. Knowledge Integration - Context-aware suggestions based on account history
3. Validation Layers - Clear visibility into AI reasoning (transparency > black box)
Day 1.
Day 1.
The Failed
Approach
The Failed
Approach
Spent: 4-5 hours, ~1000s of tokens
Built: Prototypes that didn't match the vision
Learned: Speed without planning is expensive
Outcome: ❌ Not usable
Day 2.
Day 2.
Planning +
Building with Tools
Planning +
Building with Tools
Spent: 6-7 hours, strategic token use
Built: Functional prototype with proper architecture
Learned: Plan mode + Skills + Figma connector = exponential efficiency gains
Outcome: ✅ Working prototype, ready for feedback
Day 3.
Day 3.
Refinement + Validation
Refinement + Validation
Spent: 3-4 hours, mostly polishing prompts
Built: Production-ready prototype
Validated: With PMs, executives, and a group of sellers
Outcome: ✅ Exceeds expectations, ready to show leadership
LESSON LEARNED:
Proper plan + the right tools,
the building phase became efficient.
Proper plan + the right tools,
the building phase became efficient.
30-minute planning conversation saved me
days of wasted building.
30-minute planning conversation saved me
days of wasted building.
What this
meant practically
What this
meant practically
1.
1.
Instead of describing the design in text (losing details, spending tokens), Claude could see my Figma file
2.
2.
Instead of writing boilerplate code from scratch, I used pre-built patterns (Skills)
3.
3.
Instead of iterating blindly, I had a clear spec to build against
IMPACT ON PRODUCT: Alignment Achieved
What Changed
What Changed
1.
For Stakeholders:
1.
For Stakeholders:
Executives went from "is this possible?" to "we're shipping this"
Product managers saw exactly how the AI would interact with sellers in real scenarios
Engineering had a working reference implementation (not production code, but proof of concept)
2.
For the Project:
2.
For the Project:
We didn't waste months writing detailed requirements—the prototype was the requirement.
Roadmap confidence increased from uncertain to committed
Engineering had a clear path forward
3.
For the team:
3.
For the team:
We established a new playbook: Design → Prototype in Claude Code → Engineer (instead of Design → Specs → Engineer)
Designers learned they could build, not just design
The team saw AI as an accelerant, not a threat
When and How
to Use AI Prototyping
When and How
to Use AI Prototyping
THE CORE TRUTH:
Garbage In → Garbage Out
Garbage In → Garbage Out
You can't shortcut planning. If you jump into Claude Code without a clear mental model of what you're building, you'll waste tokens, time, and momentum.
I learned this the hard way on Day 1.
You can't shortcut planning. If you jump into Claude Code without a clear mental model of what you're building, you'll waste tokens, time, and momentum.
I learned this the hard way on Day 1.
The difference between wasted effort and efficient building: A 30-minute planning session.
When AI
Prototyping Wins
When AI
Prototyping Wins
Use
Claude Code prototyping when:
Use
Claude Code prototyping when:
You have a clear design vision (Figma designs, user research or problem statement)
You need speed (timeline pressure, executive alignment, risk reduction)
The goal is validation, not production (proof of concept, stakeholder alignment, user testing)
You want to bridge designer-engineer gap (show engineers what "done" looks like)
Don't use
Claude Code prototyping for:
Don't use
Claude Code prototyping for:
Unstructured exploration (if you don't know what you're building)
If you do not have any plan or vision in mind (start with sketches/wireframes or PRDs)
Complex integrations (connecting to real databases, APIs, payment systems requires engineering)
Systems that need human judgment override (always validate AI-generated decisions with users)
The Bottom Line
The Bottom Line
While the AI provides the heavy lifting of data synthesis, the seller remains the strategic lead, empowered by precise insights.
While the AI provides the heavy lifting of data synthesis, the seller remains the strategic lead, empowered by precise insights.
What made this
prototype possible
What made this
prototype possible
✅ Clear problem statement (alignment under time pressure)
✅ Design vision already in place (Figma designs)
✅ Strategic planning (Plan mode before building)
✅ Right tools (Skills, Figma connector, Claude Code)
✅ User validation (immediate feedback shaped refinements)
What would
have failed?
What would
have failed?
❌ Jumping into code without a plan (Day 1 mistake)
❌ Trying to hand off to engineering without proof (too risky)
❌ Waiting for engineering to build a prototype (too slow)
❌ Assuming AI could figure out the vision (it can't—you have to lead)
NEXT STEPS:
The prototype shows:
The prototype shows:
Real conversational flows between sellers and the AI
Account context integration
Meeting preparation workflows
User feedback mechanisms
Try it yourself. See what's possible when planning + AI + design come together.
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