Using AI Summarization for Research Without Losing the Evidence
Research summaries that drop the evidence are useless. Here is the prompt structure that keeps the citations, sample sizes, and limitations. I read a lot of research for work: academic papers, industry reports, long-form journalism. AI summarization handles the volume, but the default summaries drop exactly what I need: the sample size, the methodology, the limitations, and the specific numbers. A summary that says Real-World Lessons I want to share a story from last month that illustrates why this matters more than you might think. A colleague asked me to review their implementation. It looked correct on the surface, but when I dug into the edge cases, I found three issues that would have caused problems in production. The first was a race condition that only appeared under load. The second was a missing error handler that would crash the service if an external API timed out. The third was a memory leak that grew slowly over 48 hours before causing an out-of-memory error. None of these issues showed up in testing because the tests used small inputs and ran quickly. The race condition needed concurrent requests to trigger. The missing error handler needed a network timeout, which never happened in the test environment. The memory leak needed hours of continuous operation to become visible. I now run a soak test on every service before deployment: 1000 requests per minute for 24 hours.