AIFit

Overview

AIFit helps product teams evaluate AI feature ideas across four dimensions:

AI Fit
Commercial Upside
Risk Burden
Evidence Readiness

The tool generates structured recommendations, human review workflows, and validation plans to support responsible AI product decisions.

The Problem

Many AI product discussions focus on capability and feasibility:

Can the model do it?
Is the technology available?
How quickly can we ship it?

Less attention is often given to:

whether AI is the right solution;
how the feature may influence human decisions;
what risks should be reviewed before launch;
how the feature should be validated.

I build AIFit to explore a more systematic approach to evaluating new AI product features.

The Solution - AIFit

AIFit combines structured feature inputs, risk-specific guidance, and LLM evaluation to generate a decision support tool.

The goal is not to automate product decisions, but to help teams ask better questions before committing resources.

System Architecture

AIFit transforms an AI feature idea into a risk-aware decision-support report through structured evaluation, risk-specific guidance, and human review planning.

Key Design Decisions

Risk-specific Evaluation

AIFit identifies a dominant risk type and adpats review and validation guidance accordingly.

Examples of risk types include:

False Validation
Emotional Vulnerability
Financial Manipulation
Fairness / Bias
Privacy / Data Sensitivity

Human-in-the-loop By Default

The tool generates recommendations, but all outputs are framed as decision support rather than automated decisions.

Explainable Outputs

AIFit provides:

Risk Classification
Risk Themes
Risk Rationale
Review Workflows
Validation Workflows

to make recommendations easier to interpret and challenge.

Example Outputs

Evaluation Summary Output

Build Boundaries Output

Human Review Workflow Output

Validation Workflow Output

Lessons Learned

Building AIFit reinforced several key ideas:

Prompt design is product architecture.
Risk evaluation requires domain-specific guidance.
Human review and validation are product features, not compliance afterthoughts.
Explainability is often more valuable than additional model complexity.