30 years watching AI projects fail because consultants build systems instead of workflows that work for real people. I teach teams to think differently about automation – solving actual problems, not imaginary ones.
Your AI gives mediocre answers because you’re asking it to solve complex problems all at once. Break tasks into smaller chunks instead – like building a web scraper, then analyzing data, then generating a report separately. Each piece gets the model’s full attention and drastically improves quality.
Most leaders think AI is just chatbots, but they’re missing 3x efficiency gains. After 30 years building AI workflows, I’ve learned the best systems understand people, not just data.
Breaking complex AI tasks into smaller chunks gets way better results than asking it to solve everything at once. Here’s my 30-year coding process for decomposition prompting.
After 30 years of coding, I’ve seen workflows evolve from rigid rule-based systems to adaptive AI agents that make real-time decisions and optimize themselves.
Recent Comments