Short instructional piece arguing that clearer, higher-quality prompts produce better outputs from LLMs (ChatGPT, Grok, Gemini, etc.). Uses the delegation-to-team analogy: poor results often reflect incomplete instructions from the requester. Advocates prompt engineering, providing context and examples to improve first-time accuracy and consistency.
The better the prompt, the better the instructions you give, the better the result you'll get. We should think about using a tool like ChatGPT or Grok or Gemini or whatever is your preferred LLM. Think of it as having a conversation with a personal assistant or a team member. When we delegate stuff we know that if we don't give full instructions we often get back not quite what we wanted and then we end up having to go backwards and forwards ask them to do it again. And very often when we don't get the result we want from a team member it's often our fault because we haven't properly explained it. And it's just the same with tools like ChatGPT. The better that we explain it, the more context examples that we give, the better the result we'll get first time and the more consistency we'll get in our results. So I'm a big fan in creating good quality prompts using prompt engineering.
Effective Pricing Training: Mastering Software Settings for Optimised Pricing
Can Pricing Be Done in Writing? How to Handle Value Conversations Without Calls
