Most developers start their AI journey by building a wrapper around a prompt. You send a request, you get a response, and you hope it is correct. This is a linear workflow. It works for simple tasks, but it falls apart when you try to produce high-quality technical documentation or SEO-driven content.
When I first tried to automate our blog's technical deep-dives, I used a massive prompt. I told the LLM to "be an expert engineer, use a professional tone, and include code examples." The result was generic fluff. It looked like a marketing brochure because the LLM was trying to guess the right answer in one shot.
To fix this, you need to move from a prompt to an agentic workflow. This means moving from a single call to a loop of planning, executing, and critiquing.
The Problem with Linear Prompts
Linear prompts suffer from the "hallucination drift." If the model makes a mistake in the second paragraph, it will spend the rest of the article trying to justify that mistake to maintain internal consistency. It cannot go back and fix its own logic because it only moves forward.
