Hello, tech innovators, data nerds, and health-tech visionaries! 👋 Welcome to the ultimate engineering deep-dive of Med AI.
If you followed our journey in Round 1, you know we laid the groundwork by analyzing how raw brute-force data parsing heavily chokes LLM context windows and spikes infrastructure bills. But we didn't stop there. We got selected in top 15 for Round 2, we took the baseline prototype and scaled it into a monster: benchmarking three entirely different retrieval architectures against a massive, custom-generated 100 Million Token Dataset.
Here is the continuation of how we evolved Med AI from a local hack into a hyper-scale clinical intelligence suite. 🏎️💨
⏪ Round 1 Retrospective: The Genesis of Med AI
In the first round, our mission was simple but brutal: prove that standard linear search methods break down when processing large-scale medical data. We built our initial System Auditor UI to load raw CSV medical files straight into local RAM. While the clinical summaries generated by the LLM were highly detailed, the system ground to a halt under load.
