People struggle with AI prompts because the model lacks background on their goals and progress. The solution is 'Context Engineering': creating an environment where the AI continuously accumulates user-specific information, materials, and intent, reducing the need for constant prompt tweaking.

Use an AI assistant like Claude Code to create a persistent corporate memory. Instruct it to save valuable artifacts like customer quotes, analyses, and complex SQL queries into a dedicated Git repository. This makes critical, unstructured information easily searchable and reusable for future AI-driven tasks.

By training AI on your personal data, arguments, and communication style, you can leverage it as a creative partner. This allows skilled professionals to reduce the time for complex tasks, like creating a new class, from over 16 hours to just four.

Simply offering the latest model is no longer a competitive advantage. True value is created in the system built around the model—the system prompts, tools, and overall scaffolding. This 'harness' is what optimizes a model's performance for specific tasks and delivers a superior user experience.

Instead of prompting a specialized AI tool directly, experts employ a meta-workflow. They first use a general LLM like ChatGPT or Claude to generate a detailed, context-rich 'master prompt' based on a PRD or user story, which they then paste into the specialized tool for superior results.

To effectively learn AI, one must make a conscious mindset shift. This involves consistently attempting to solve problems with AI first, even small ones. This discipline integrates the tool into daily workflows and builds practical expertise faster than sporadic, large-scale projects.

Consolidate your values, goals, and principles into a single document. Upload this "master prompt" to an AI before any query, ensuring all responses are tailored to your unique context. This transforms a generic tool into a personalized advisor that understands you deeply.

Instead of holding context for multiple projects in their heads, PMs create separate, fully-loaded AI agents (in Claude or ChatGPT) for each initiative. These "brains" are fed with all relevant files and instructions, allowing the PM to instantly get up to speed and work more efficiently.

AI tools compound in value as they learn your context. Spreading usage across many platforms creates shallow data profiles everywhere and deep ones nowhere. This limits the quality and personalization of the AI's output, yielding generic results.