AI-Assisted Product Assessment
A structured framework for evaluating technology products using AI models as research assistants, benchmark tools, and decision-support systems.
Product assessment has traditionally been a slow process — market interviews, analyst reports, internal workshops. AI models have compressed this timeline dramatically without sacrificing quality.
The approach we use starts with structured prompting: defining the evaluation criteria, competitive landscape, and key risk dimensions before running any model. This prevents the model from optimising for impressiveness over accuracy.
Claude 3.5 is our primary tool for synthesising long technical documents and vendor materials. GPT-4o handles the comparative scoring matrices. We cross-check findings with Gemini's Deep Research feature when we need citation-backed analysis.
The human consultant's role shifts to adversarial review: challenging model outputs, surfacing gaps, and injecting tacit knowledge the model cannot have. This combination consistently outperforms either AI alone or human alone on structured product assessments.
- Define evaluation rubric before engaging the model.
- Use multiple models and compare outputs to reduce hallucination risk.
- Always have a human review layer for vendor-specific scoring.
- Document model versions and prompts used for reproducibility.