AI's Brand Recommendation Process
In this monologue, Emilio Garcia outlines how AI systems, particularly large language models (LLMs), generate brand and service recommendations by mirroring human decision‑making based on reputation and trust. He explains that the AI is first pre‑trained on data from reputable sources, which equips it with an internal list of likely trustworthy brands for a given category (e.g., employee‑management tools). When a user queries the AI, it does not simply scrape the web for an answer; instead, it draws on this pre‑trained knowledge to propose candidate brands and then searches the internet—especially review sites—to find supporting evidence that validates its suggestions. This two‑step process (pre‑trained candidate generation followed by evidence gathering) creates a “chore list” akin to how humans evaluate brands, fostering user trust in AI recommendations.
