An article on HackerNoon explains that LLMs can understand product names correctly yet still return wrong matches, because similar product names, identical products, and commercially acceptable substitutes are three different things. It gives the example of “Apple Juice, 1L, No Added Sugar” versus “Apple Juice Drink, 1L, Sweetened”: lexical similarity is high and both are apple-based drinks, but one is juice with no added sugar while the other is a sweetened juice drink, so the pair should be rejected for an exact match. Conversely, “Extra Virgin Olive Oil, 750 ml” and “EVOO, 0.75L” look different, but EVOO is a common abbreviation and the volumes are equivalent once normalized, so the pair could be a valid match if other relevant attributes match. The article says an algorithm can be very good at measuring textual similarity without understanding whether two products are actually equivalent, which requires looking at product attributes and the requirements of the matching task; sometimes the business needs an acceptable substitute rather than an identical product. It also notes that for a product listed as “Premium Coffee, 250g,” the manufacturer, roast level, and coffee variety are unknown, and an LLM receiving only the original product name lacks that information.