Don't skip documentation

In the era of instant, AI-generated answers, it is easy to assume that technical documentation is becoming obsolete. After all, why struggle through a manual when an LLM can summarize a concept in seconds? However, relying solely on AI for technical guidance is a dangerous shortcut. The reality is that documentation is more vital than ever, precisely because LLMs are not "up to date."

The core issue lies in the training cycles of generative models. LLMs are snapshots of the past; they lack real-time awareness of your specific environment, recent framework updates, or the unique, proprietary architectural decisions made within your organization. When you ask an AI about a tool released last month or a critical security patch from yesterday, the model often resorts to "hallucinations"—confidently synthesizing plausible but incorrect information.

Documentation serves as the ground truth that AI requires to function reliably. Instead of replacing docs, LLMs are now evolving into engines that index and retrieve the information we provide. If your documentation is missing or outdated, you are essentially starving the AI of the data it needs to assist you accurately.

Furthermore, documentation is an act of knowledge transfer, not just data storage. Writing down technical decisions forces clarity, uncovers edge cases, and creates a record of intent. An AI might tell you how a function works, but it cannot explain the why—the trade-offs, the business context, and the history behind your team’s choices.

By prioritizing clear, structured documentation, you aren't just helping your team; you are anchoring your AI tools in reality. Documentation is the bedrock of accuracy—without it, your AI is just an eloquent guesser.