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Computer vision projects rarely go exactly as planned, and this one was no exception. The idea was simple: Build a model that could look at a photo of a laptop and identify any physical damage — things like cracked screens, missing keys or broken hinges. It seemed like a straightforward use case for image models and large language models (LLMs), but it quickly turned into something more complicated.
Along the way, we ran into issues with hallucinations, unreliable outputs and images that were not even laptops. To solve these, we ended up applying an agentic framework in an atypical way — not for task automation, but to improve the model’s performance.
In this post, we will walk through what we tried, what didn’t work and how a combination of approaches eventually helped us build something reliable.
Where we started: Monolithic prompting
