1. Prepare a coherent dataset
Do not mix unrelated products, marketplaces, or variations without labeling them. Keep rating, date, verified-purchase status, helpful count, variation, review ID, and body when those fields matter to the question. Inspect completion metadata so you know whether the export reached the requested scope or stopped early.
2. Minimize data before uploading
INCIO creates the export locally, but a third-party AI service receives whatever you upload. Remove reviewer display names, URLs, or other fields you do not need. Check the provider's privacy, retention, training, and enterprise-data terms before using customer-feedback data.
3. Ask for auditable output
A strong request defines the task, evidence rules, and output format:
Ask the model to cite review IDs, count rows, distinguish direct statements from interpretation, identify contradictions, and state what the dataset cannot establish. Avoid vague prompts such as "summarize the reviews."
4. Verify important findings
- Open every cited review ID and confirm the claimed meaning.
- Recount high-priority categories in the source file.
- Check whether one variation or date range drives the pattern.
- Look for positive reviews that contradict the proposed conclusion.
- Repeat the analysis with a narrower prompt when categories overlap.
5. What AI review analysis cannot prove
A model summary does not prove product safety, legal compliance, market demand, financial viability, or a representative defect rate. It may misclassify sarcasm, merge different failure modes, overlook context, or invent support. Use it to accelerate coding and hypothesis generation, not to replace source review, testing, or professional advice.