Clause for Concern: When Your AI Stops Making Sense
A new approach to model training helps AI generalize better, adapt faster, and scale more effectively across unpredictable use cases.
At Clause & Effect, a fictional LegalTech company known for its AI-powered contract analysis platform, business was booming. The company had carved out a niche helping boutique law firms and in-house legal teams cut contract review times in half. Its AI assistant, Briefly, had been fine-tuned on thousands of financial and healthcare agreements, trained to flag risky clauses, summarize key obligations, and even suggest redlines. Clients loved the speed. Legal teams finally had a tool that worked almost like a junior associate… but never took a vacation.
Kiwi, a fictional former attorney turned AI product lead, was one of the minds behind Briefly. With a JD/MBA background, Kiwi bridged legal intuition and product strategy. Under his leadership, Briefly had grown from a promising prototype to a core product. Clients across the board praised its ability to “understand” contracts in dense regulatory domains (at least, until things started to change).
When Success Starts to Strain
The first issues appeared as whispers from the support team. A few high-value clients were seeing unexpected misses. Briefly had failed to flag an unusual indemnity clause buried deep in a contract for a robotics startup. Another client reported that Briefly misinterpreted a clause from a new AI governance policy. In both cases, the clause was structurally similar to those already in the training data, but used unfamiliar phrasing and terminology.
What concerned Kiwi wasn’t just the mistakes; it was where the model was failing. These weren’t extreme edge cases or highly technical legal doctrines. They were standard clauses, reworded slightly or pulled from sectors outside the model’s fine-tuned training base. Kiwi knew that in-context prompting could often handle such variations. But Briefly had been designed for high-speed, automated workflows, and prompting didn’t scale well in production environments.
Meanwhile, the pressure was mounting. Clause & Effect had just signed a major strategic partnership with a global legal services provider. The deal meant onboarding contract types from international jurisdictions and new sectors like biotech and crypto. The executive team saw this expansion as a critical moment… a first-mover opportunity that could cement the company’s market leadership.
But internally, Kiwi saw a bottleneck. Each new contract format required fine-tuning. Each fine-tuning cycle took time, money, and manual QA. Worse, the improvements were incremental and often brittle. A clause would be added to the training data, and the model would learn that clause, but it wouldn’t generalize the logic to other versions. One step forward, half a step sideways.
What’s at Stake When AI Stops Learning the Right Way
Kiwi had seen this pattern before: when machine learning (ML) systems become overfit, they stop thinking and start memorizing. Briefly was becoming more like a parrot than a paralegal.
This was more than a technical inconvenience; it was a business risk. If Briefly couldn’t adapt to new contract formats quickly, clients would start to churn. More concerning, mistakes in clause detection could create real legal exposure. A missed liability cap or misinterpreted exclusivity clause wasn’t just embarrassing; it was grounds for litigation or financial harm.
The stakes weren’t just external. Internally, engineering costs were ballooning. Each iteration of fine-tuning required more training data, more testing, and more compute resources. Speed to market was slowing, and the AI team was increasingly stretched thin. There was a growing sense that Clause & Effect was patching symptoms, not solving the core problem.
Kiwi found himself facing a familiar business dilemma with a modern twist: how do you scale a smart product without letting it get dumber under pressure?
Something had to change, not just in how the model was trained, but also in how the team thought about learning itself.
Curious about what happened next? Learn how Kiwi leveraged a recently published AI research from Google Deepmind and Stanford University, reframed the problem with a smarter learning strategy, and achieved meaningful business outcomes.