How to Get Useful Summaries from AI Without Losing the Detail
AI summarizers are fast but they often drop the detail I actually need. Here is the workflow I use to keep what matters. I summarize a lot of material in my work: research reports, meeting transcripts, long Slack threads pulled out for sharing. AI summarization tools handle the volume, but the default summaries often miss what I needed to keep. A meeting summary that lists the topics covered but drops the decision and the owner is useless to me. Over the past year I have built a workflow that gives useful summaries instead of generic ones. Decide What You Need Before Summarizing The biggest mistake is feeding an article into a summarizer without specifying what should be preserved. If I summarize a research report using the default prompt, I get a paragraph hitting the main findings. That works when I just want the headline. It fails when I needed the methodology, the sample size, or the limitations because the model decided those were not important. Before summarizing, I write one sentence about what I need from the source. For a research report I might say I need the findings, the sample size, the methodology, and any limitations the authors mention. For a meeting I might say I need decisions made, action items with owners and deadlines, and any unresolved questions. That sentence becomes the first line of my prompt. The model now knows what to keep.