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The case for context engineering in clinical AI — and how Tryal Accelerator is built around it. There's a persistent myth in the AI space: give the model more information and you'll get better answers. It sounds intuitive. It's also wrong. A 2025 research study by Chroma  tested 18 frontier language models — including GPT-4.1, Claude, and Gemini — and found that every single one performed worse as the amount of input grew . In some cases, models that scored 95% on shorter inp

What if much of that work could be completed in a fraction of the time using AI-driven tools, structured templates, and automated research?

TRYAL ACCELERATOR | FEATURE DEEP DIVE Shae Wilkins | March 2026 | 6 min read The Ripple Effect of Change Clinical trials don't stand still. Protocols are amended, visits are added, regulatory feedback requires edits. And every change creates a cascade of documentation updates. The challenge isn’t making the initial change—it’s identifying everywhere that change needs to propagate. Miss one document, and you’ve created an inconsistency that could raise questions from regulator

To get real value from AI-enabled tools, the most important factor isn’t the model — it’s the context . The quality of AI-generated content is directly tied to the quality and relevance of the information you give it. Even the most advanced AI is constrained by its knowledge. When context is incomplete, outdated, or missing; outputs like drafts, summaries, or insights will reflect those gaps. Curating current, accurate study-specific materials is the single most effective way