How to reverse-engineer the perfect ChatGPT prompt, according to an MIT professor

Crafting the perfect prompt for artificial intelligence is both an art and a science, according to Andrew Lo, director of MIT’s Laboratory for Financial Engineering. Speaking during a recent web presentation for Harvard University’s Griffin Graduate School of Arts and Sciences, the professor outlined some key rules for getting high-quality responses from your AI chatbot.

“I think that there's a real art and science to prompt engineering. In fact, there is a job description now called prompt engineers. ” Lo said

A vague prompt like “How should I retire?” may seem reasonable for a human, but Lo argues it accounts as a ‘bad prompt’ for an AI model. He argued that a good prompt will contain enough details so that the large language model (LLM) - brains behind chatbots like ChatGPT and Gemini - can get enough details to provide you with an appropriate response.

“If you don't provide that detail, they'll just give you generic gobbledy gook that will not be particularly helpful.” he noted

What is a good prompt then? Lo gave an example one such prompt:

“Assume you are a fee-only fiduciary advisor. Here are my goals, constraints, tax bracket, state, assets, risk tolerance and timeline. Provide me with, number one: base case strategy. Number two: key assumptions. Three: risks. Four: what could invalidate this plan. Five: what information you are missing, and in particular, what are you uncertain about.”

“It does actually make sense to spend a little bit of time away from the computer and making a list of your questions,” Lo said, comparing it to preparing for a meeting with a doctor, lawyer or accountant

“Always ask the LLM, what are you uncertain about? What information are you missing? because you want to understand the limitations of what they come up with.” he noted

“I will then ask the LLM, can you please tell me what prompt I should have used in order to generate the final answer that I was really looking for,” he noted