AI to the Rescue: Letting AI Handle Repetitive Explanations
Sometimes, no matter how clearly you explain a technical detail, it just doesn't land. At that point, I decided to try something new: let AI handle the explanation for me.
The Problem
In a recent chat, I found myself explaining—again and again—how a specific system distinguishes between two similar concepts, and why a particular configuration wasn't working as expected. Despite clear, repeated explanations (and even a direct solution), the confusion persisted.
The Experiment
Instead of typing it out one more time, I copied the last several chat messages and asked AI to summarize and answer the question directly. I requested a clean, simple explanation focused on:
- The core distinction between the two concepts
- Why the previous advice was correct
- How the configuration values affect behavior
The AI delivered exactly what I needed—no extra fluff, just a clear, actionable answer I could paste back into the chat.
To make it clear, I opened my reply with: "providing AI with the last chat messages: (hope this does help?)"
Maybe this is a new productivity trick—letting AI handle the repetition and freeing up time and patience for more important work. It felt a bit rude, but the questions showed lack of high level understanding of basic functionality so I felt this may be appropriate.
On the flip side, I've recently experienced receiving AI-generated requirements and problem reports myself. The quality and clarity were amazing—structured, concise, and focused. Reviewing, analyzing, or resolving these was easier than ever before. When AI is given the right context, it can produce documentation and feedback that truly accelerates collaboration and problem-solving.
Practical Example: This workflow is especially powerful for transforming lengthy chat discussions—such as those debating bugs or feature adaptations—into clear, actionable stories for the development team. By feeding the AI the relevant chat excerpts, you can quickly generate concise user stories, bug reports, or feature requests that capture the core issue, proposed solutions, and decision rationale. This not only saves time but also ensures that important context and agreements from the discussion are preserved and communicated unambiguously to all stakeholders.