One of the biggest friction points in digital commerce is product comparison. Shoppers want clarity before they commit, for instance, a request like “Compare iPhone 15 vs Galaxy S24 battery/camera” is very common.

Customers expect side-by-side, spec-level comparisons, but the reality is that specs live in multiple places: manufacturer datasheets, expert reviews, and unstructured product pages. Traditional search and recommendation systems struggle to bring all of this together in a way that is both accurate and up to date at the attribute level.

This is where RAG comes in.

Product data can be broken down into granular chunks (for example, one chunk for battery, one for camera), and stored in a vector database with rich metadata. When a user asks for a comparison, the system deconstructs the query into products and attributes, pulls the relevant specs and reviews, and then uses an LLM to synthesize a structured output. The response can take the form of side-by-side tables, markdown summaries, or even highlights of relative strengths and weaknesses.

Of course, implementing this is not without challenges. There is the granularity trade-off; chunks that are too small lose context, while chunks that are too large dilute relevance.

Data freshness is another issue, since specs and reviews change quickly and ingestion pipelines need to keep up. Multi-product queries must be handled consistently across attributes, not in isolation. And qualitative reviews can introduce bias, which requires normalization before use.

The business impact, however, is powerful. A system like this boosts buyer confidence, reduces decision churn, and ultimately improves conversion rates. Customers trust platforms that explain choices transparently, not just recommend blindly.

RAG, in this context, bridges the gap between unstructured content and structured buyer needs, turning overwhelming product specs into actionable clarity.