Amazon Optimisation
Rufus: How Amazon’s In-House AI Understands Your Questions
So how does Rufus actually “understand” shopper queries and respond with helpful, relevant answers? The answer lies in semantic similarity models. Algorithms designed to match what a user means with the most relevant product or answer available.
13 August 2025
8 min read
At the core of Rufus is a Semantic Similarity Model, but the heavy lifting happens through a multi-step process:
Noun Phrase Identification
Every shopper question or answer is broken down into its key noun phrases (e.g., “wireless headphones with noise cancellation”).
Question & Answer Matching
Rufus compares the noun phrases from the user’s query to a set of trained answers and previously asked questions. It then runs semantic similarity scoring, essentially checking how closely each piece of language matches in meaning, not just in wording.
Training Across Inputs
The system trains separately on questions, answers, and full Q&A pairs. This builds a robust understanding of natural language, including nuance, slang, and shopping-specific context.
Scoring and Ranking
Each potential answer is given a similarity score. The most semantically aligned answer rises to the top and is shown to the shopper.
Why This Matters for Brands
For brands, this shift to meaning-based ranking means traditional keyword stuffing won’t cut it anymore. Instead, your product content - from bullet points to Q&A - needs to clearly communicate intent, features, and use cases in language that aligns with how real people speak.
If Rufus doesn’t “understand” your product, it won’t recommend it - even if you’re the perfect match.
How LMO7 Helps
We help brands audit and optimise their content not just for Amazon’s A10 algorithm, but for Rufus and other emerging LLM-based surfaces. That means:
- Semantic matching reviews of your listings
- Q&A content recommendations
- Image OCR checks to ensure visuals are readable by AI
- Competitive benchmarking using real queries
The age of AI-powered search is already here. Make sure your brand is showing up when customers ask the most important question: What should I buy?
At LMO7, we’re focused on how AI is changing discovery and conversion. Rufus, Amazon’s in-app shopping assistant, is a prime example of this evolution.
Noun Phrase Identification
Every shopper question or answer is broken down into its key noun phrases (e.g., “wireless headphones with noise cancellation”).
Question & Answer Matching
Rufus compares the noun phrases from the user’s query to a set of trained answers and previously asked questions. It then runs semantic similarity scoring, essentially checking how closely each piece of language matches in meaning, not just in wording.
Training Across Inputs
The system trains separately on questions, answers, and full Q&A pairs. This builds a robust understanding of natural language, including nuance, slang, and shopping-specific context.
Scoring and Ranking
Each potential answer is given a similarity score. The most semantically aligned answer rises to the top and is shown to the shopper.
Why This Matters for Brands
For brands, this shift to meaning-based ranking means traditional keyword stuffing won’t cut it anymore. Instead, your product content - from bullet points to Q&A - needs to clearly communicate intent, features, and use cases in language that aligns with how real people speak.
If Rufus doesn’t “understand” your product, it won’t recommend it - even if you’re the perfect match.
How LMO7 Helps
We help brands audit and optimise their content not just for Amazon’s A10 algorithm, but for Rufus and other emerging LLM-based surfaces. That means:
- Semantic matching reviews of your listings
- Q&A content recommendations
- Image OCR checks to ensure visuals are readable by AI
- Competitive benchmarking using real queries
The age of AI-powered search is already here. Make sure your brand is showing up when customers ask the most important question: What should I buy?
At LMO7, we’re focused on how AI is changing discovery and conversion. Rufus, Amazon’s in-app shopping assistant, is a prime example of this evolution.