Customer Experience & Conversational AI Practical insights
Ask product constraints before AI recommendations
Build a product advice chatbot that checks intended use, compatibility and budget before recommending items from verified catalog and availability data.
A product recommendation is useful only if the item fits the customer’s situation. A conversational assistant can gather that situation in ordinary language, but it needs verified catalog facts and a clear distinction between essential requirements and preferences. Start with suitability before ranking attractive options.
Identify the deal breakers
Ask about the constraints that can rule products out: dimensions, compatibility, required functions and budget. Gather only what matters to the category. For a fictional desk purchase, room width and seating needs can matter more than a broad lifestyle profile. Do not treat an inferred preference as a confirmed requirement.
Separate requirements from preferences
Repeat the essential constraints briefly and invite correction. Then ask about softer preferences such as finish or style when useful. If no item meets all requirements, explain the conflict. Suggest which constraint the customer could reconsider without silently discarding the budget or recommending an incompatible product.
Explain the shortlist with evidence
Show a small set of suitable options and state why each fits. Ground dimensions, materials and compatibility in catalog records, and check dynamic facts through an appropriate current source. Google’s 2026 shopping announcement illustrates the direction of conversational commerce; specific capabilities and access vary. [1] This workflow is a design proposal, not a claim that any catalog becomes reliable by connecting to an AI interface.
Test difficult shopping tasks
Include missing dimensions, contradictory product records and requests outside the available range. Ask reviewers whether the assistant exposes trade-offs and admits when information is insufficient. Track unsuitable recommendations and the reasons customers reject options, not just clicks. A commercially valuable assistant helps people make a defensible choice, including deciding that the current assortment does not meet their needs. Use those gaps to improve product information before increasing recommendation volume.
Sources and evidence
Sources checked on 4 October 2026. Proposed workflows and hypothetical examples are editorial analysis.
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