Building the MVP

The One-Workflow Rule

Your MVP does one thing. Not three features. Not a platform. One workflow, end to end, from input to output. If your product summarizes documents, the MVP takes a PDF and returns a summary. That's the entire product on launch day.

The temptation with AI is to build a "do anything" tool. Resist this. ChatGPT already exists. Your product wins by being the best at one specific job, not mediocre at twenty.

The AI MVP Checklist

Input capture: How does the user give you data? File upload, text input, API connection, or screenshot. Make it frictionless. Every extra step is a drop-off point.

Processing: Your AI pipeline. Prompt engineering, context assembly, model call, output parsing. This is your engine. Optimize for reliability over cleverness.

Output delivery: How does the user get the result? In the UI, via email, as a downloadable file, through a Slack notification. Match the delivery to the user's workflow — don't make them come to you.

Error handling: AI fails. Models hallucinate. Tokens run out. Your MVP must handle these gracefully. A clear error message and a retry button are minimum requirements.

A complete AI MVP in 30 lines.

Here is the entire "AI Meeting Notes" MVP — from audio upload to structured output. This is a real, shippable product:

import anthropic
from pathlib import Path

client = anthropic.Anthropic()

def process_meeting(transcript: str) -> dict:
    # One prompt. One model call. One workflow. That's the MVP.
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=2000,
        system="""You are a meeting notes assistant. Extract structured notes.
Return JSON with exactly these keys:
- summary (2-3 sentences)
- decisions (list of decisions made)
- action_items (list of {owner, task, deadline})
- key_topics (list of topics discussed)""",
        messages=[{"role": "user", "content": transcript}]
    )
    return json.loads(response.content[0].text)

# That's it. Input → AI → Output. Ship it.
notes = process_meeting("Sarah: Let's move the launch to March 15...")
# → {"summary": "Team agreed to delay launch...",
#    "action_items": [{"owner": "Sarah", "task": "Update timeline", "deadline": "March 10"}],
#    ...}

Handling AI Failures

Your AI will be wrong sometimes. This isn't a bug — it's a fundamental property of probabilistic systems. The question isn't how to prevent failures, but how to design for them.

Show confidence levels when appropriate. "I'm 90% sure this is a receipt for office supplies" is better than asserting it as fact. Let users correct errors easily — an "edit" button next to every AI output.

Never delete the original. If your AI transforms, summarizes, or categorizes something, always keep the source accessible. Users need to verify. Make verification easy, not insulting.