laptop-recommendation-engine.mdx
Interactive laptop recommendation engine
An LLM-assisted recommendation engine grounded in a database of real laptop products.
Overview
The laptop recommendation engine is a vertical slice for helping users translate natural-language requirements into grounded laptop suggestions. The current version is intentionally limited to laptops and does not claim support for broader product categories yet.
The problem
A user might ask for a laptop for programming, college, gaming, travel, battery life, or a strict budget. Those requests are ambiguous, but the recommendation cannot be ambiguous: it needs to return real products with clear reasons.
Architecture
The system separates interpretation from ranking. The language model helps extract budget, use case, and preference signals. The ranking layer then applies grounded filters and scoring rules against the laptop database.
| Stage | Responsibility |
|---|---|
| Intent interpretation | Convert user language into structured constraints |
| Product retrieval | Select candidate laptops from real records |
| Ranking | Balance budget, performance, battery life, and portability |
| Explanation | Describe why each result fits the request |
Technical decisions
Keeping the LLM away from final product invention is the central design choice. It can explain, classify, and extract, but the product list remains database-backed.
Current state
The project is receiving finishing touches. Live and source links will appear automatically after real URLs are added to the frontmatter.
Lessons learned
Recommendation systems need product discipline as much as model capability. Separating model interpretation from deterministic product ranking makes the application easier to test, easier to explain, and safer for users.
Screenshots
