For niche tasks, leverage an AI model with deep domain knowledge (like Claude for its own 'Skills' feature) to create highly specific prompts. Then, feed these optimized prompts into a powerful, generalist coding assistant (like Google's) to achieve a more accurate and robust final product.

Many users blame AI tools for generic designs when the real issue is a poorly defined initial prompt. Using a preparatory GPT to outline user goals, needs, and flows ensures a strong starting point, preventing the costly and circular revisions that stem from a vague beginning.

A powerful AI workflow involves two stages. First, use a standard LLM like Claude for brainstorming and generating text-based plans. Then, package that context and move the project to a coding-focused AI like Claude Code to build the actual software or digital asset, such as a landing page.

Instead of facing a blank canvas, create a custom GPT that asks a series of structured questions (e.g., product goal, target user, key flows). This process extracts the necessary context to generate a focused, high-quality initial prompt for prototyping tools.

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.

Use the Claude chat application for deep research on technical architecture and best practices *before* coding. It can research topics for over 10 minutes, providing a well-summarized plan that you can then feed into a dedicated coding tool like Cursor or Claude Code for implementation.

Use Claude's "Artifacts" feature to generate interactive, LLM-powered application prototypes directly from a prompt. This allows product managers to test the feel and flow of a conversational AI, including latency and response length, without needing API keys or engineering support, bridging the gap between a static mock and a coded MVP.

Instead of using sensitive company information, you can prompt an AI model to create realistic, fake data for your business. This allows you to experiment with powerful data visualization and analysis workflows without any privacy or security risks.

Instead of manually writing prompts for a video AI like Sora 2, delegate the task to a language model like Claude. Instruct it to first research Sora's specific capabilities and then generate prompts that are explicitly optimized for that platform's strengths, leading to higher-quality, more effective outputs.