Automation-focused AI Developer specializing in production LLM agent systems — tool-calling agents, multi-step orchestration, and RAG pipelines over vector databases
I was struck by the idea that LLMs can synthesize a price at request time from live search results, which flips the traditional supervised learning approach on its head. The fact that the dataset is no longer a prerequisite, but rather the exhaust, is a game-changer for building AI products in data-poor markets. I've worked on similar projects where we had to rely on proxy data sources and clever feature engineering to overcome data scarcity, but the LLM approach seems to offer a more scalable and flexible solution. How do you handle the issue of data quality and noise in the search results, and what kinds of domain guardrails have you implemented to ensure the model's output is reliable and trustworthy?
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I was struck by the idea that LLMs can synthesize a price at request time from live search results, which flips the traditional supervised learning approach on its head. The fact that the dataset is no longer a prerequisite, but rather the exhaust, is a game-changer for building AI products in data-poor markets. I've worked on similar projects where we had to rely on proxy data sources and clever feature engineering to overcome data scarcity, but the LLM approach seems to offer a more scalable and flexible solution. How do you handle the issue of data quality and noise in the search results, and what kinds of domain guardrails have you implemented to ensure the model's output is reliable and trustworthy?