LLM Decision-Support MVP

Project Overview

The LLM Decision-Support MVP is a concept application designed to support —not replace—human decision-making. Users can enter a business question, compare potential options, provide relevant business context, and receive a transparent, structured recommendation.

        Responsible AI approach:
        The solution surfaces assumptions, risks, confidence indicators, and
        human-review questions so stakeholders can validate recommendations before
        taking action.
      

Core Capabilities

Option Evaluation

Compares multiple business or product options against defined goals, constraints, benefits, and potential trade-offs.

Risk Identification

Highlights decision risks, missing information, dependencies, and assumptions that require stakeholder validation.

Actionable Recommendations

Produces concise recommendations, confidence indicators, and next-step actions for product, operations, and leadership teams.

Business Value

Faster Insights

Accelerates early-stage analysis by organizing complex inputs into a consistent decision framework.

Transparent Reasoning

Makes assumptions, evidence gaps, confidence levels, and key risks visible instead of presenting unsupported answers.

Human-in-the-Loop

Keeps final decisions with product owners and business stakeholders, especially for high-impact decisions.

Skills Demonstrated

Large Language Models Prompt Engineering Decision Intelligence Product Strategy Product Analytics Business Intelligence Python SQL Responsible AI Human-in-the-Loop Design

My Contribution

Defined the MVP concept, decision workflow, evaluation criteria, and responsible-AI guardrails. The project demonstrates how AI, analytics, and product strategy can be combined to help teams make more informed, traceable, and data-driven decisions.