Smarter Prompts. Better Outputs.
Less Frustration.

The sources argue that prompt engineering, while accessible and an easy entry point to AI, often leads to "erratic results" and a lack of reliability. This is because generative AI models like ChatGPT create a "new answer every time," and their responses are heavily influenced by the user's specific context and their own "training" of the AI over time. Relying solely on prompts without a structured framework makes it difficult to get consistent, repeatable, and shareable results, which is crucial for business and collaborative environments.

Context is considered "king" and the first step in the framing stack. It provides the AI with the necessary background information, goals, descriptions of products, audiences, and overall environment within which it should operate. Without proper context, AI acts like a "golden retriever that's chasing every squirrel," leading to inconsistent and unreliable results across different users and situations. It allows for repeatability and collaboration by ensuring everyone is "working in the same ballpark."

The "real power skill" is "framing." This involves a three-part "framing stack": Context, Constraint, and Calibration. Instead of focusing on individual prompt keywords or formats, the emphasis shifts to establishing a clear environment and rules for the AI to operate within, ensuring more reliable and consistent outputs.

A Custom GPT is a personalized AI environment that has been "trained" with specific information, context, and constraints. For tasks that will be used "over and over again," especially in a business or collaborative setting, creating a Custom GPT is highly recommended. It ensures consistent results across different users, helps maintain the desired format and context, and can even be trained to adopt specific conversational styles, unlike relying on open AI instances where each user's interaction trains the AI differently.