How to Prioritize Your MVP Features with AI in 2 Min. [Template Included]
Use this AI-powered framework to make data-driven MVP decisions.
That dreaded moment: deciding what goes into your MVP.
Building everything feels safe but leads to delayed launches and wasted resources. Most founders end up choosing features based on gut feel or investor pressure, resulting in bloated MVPs that miss market windows. The real cost isn't just time and money – it's the missed opportunity to learn from real users.
The difference between a successful MVP and a failed one often comes down to what you choose to leave out.
The Ideal Solution
Every startup needs a systematic way to evaluate feature priorities.
The perfect solution would combine quantitative scoring with startup-specific constraints, accounting for both market impact and development realities. It would help founders make confident decisions about what to build first, backed by data but practical enough for early-stage startups. Most importantly, it would be quick to implement and adapt as you learn.
What we need is a framework that turns subjective feature debates into objective decisions.
The RICE Framework Adapted
Rice Scoring comes from Intercom's product team, but we'll adapt it for startups.
The framework evaluates features using four metrics: Reach, Impact, Confidence, and Effort. For startups, this provides a beautiful balance between data-driven decisions and practical constraints. The key is modifying it to account for MVP-specific considerations like technical dependencies and core feature requirements.
Example RICE calculation:
Feature reaches 30% of users (0.3)
High impact score (1.0)
80% confidence (0.8)
5 days of effort
RICE score = (0.3 * 1.0 * 0.8) / 5 = 0.048
RICE gives you a numerical score for each feature, making priorities crystal clear.
The AI-Ready Template
Our template structures the prioritization process in a founder-friendly way.
The prompt combines RICE scoring with MVP-specific considerations, helping you think through both quantitative and qualitative aspects of each feature. It's designed to work with AI assistants like Claude or GPT-4, turning your rough thoughts into actionable priorities.
I am building [product name] to solve [core problem] for [target users].
My initial feature list includes:
- [List 3-5 key features you're considering, one per line]
My constraints:
Time to initial launch: [target timeline]
Development resources:
- [team size/availability]
Technical limitations:
- [any major constraints]
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Using RICE framework, evaluate each feature with:
RICE Score Components:
- Reach: How many users will this impact in a quarter [0-100%]
- Impact: Effect on user experience [0.25=minimal, 0.5=moderate, 1.0=high, 2.0=massive]
- Confidence: How sure am I about estimates [0-100%]
- Effort: Dev time needed [days, rough estimate]
Additional MVP Considerations:
- Must-have vs. nice-to-have [essential/optional]
- Technical complexity [low/medium/high]
- Dependencies on other features [yes/no, which ones]
Expected output:
- Ranked feature list with RICE scores
- Core MVP feature recommendations
- Risk assessment
- Suggested development sequence
- Estimated MVP timelineUse this template when:
You have a list of potential MVP features
You're struggling to decide what to build first
You need to justify feature priorities to stakeholders
Limitations:
Requires some market understanding to estimate reach and impact
Works best with 3-7 features at a time
May need adjustment for highly innovative products
The template isn't perfect, but it's far better than gut-based decisions.
The Real Value
Feature prioritization isn't about perfect scores – it's about confident decisions.
This framework forces you to think systematically about each feature's value, while the AI helps spot gaps in your reasoning and suggests alternatives you might have missed. It turns vague feature ideas into concrete build decisions, helping you launch faster with the right MVP.
Your MVP's success depends more on what you leave out.
Take Action Now
Open your favourite AI assistant (I’m team Claude) and paste in the template.
Run your current feature list through it.
Pay special attention to features that score surprisingly high or low – they often reveal hidden assumptions about your product.
Let's build better MVPs, one feature at a time.