Three Tactics for Improving AI Response Quality: Multipersona, According-to Source, and Mood Prompting

Unlock the power of prompt engineering: discover three easy-to-implement tactics to get better, more accurate responses from AI models like ChatGPT.

  • * Dan is co-founder of Promptuub, a tool for managing prompts designed for teams.
  • * Prompt engineering involves using specific techniques to get better and more accurate responses from language learning models (LLMs).
  • * The non-deterministic nature of LLMs makes it difficult to predict their behavior and small changes in the prompt can significantly affect outputs.
  • * Poor user experiences or unexpected model behavior can lead to loss of trust in AI features in a product.
  • * Here are three easy-to-implement tactics for better and safer responses:
  • * A research study from the University of Illinois showed that calling on various agents designed for specific tasks can lead to more accurate outputs.
  • * For example, if prompting a model to help write a book, multi-prompting would involve getting a publicist, author, and target audience involved in a brainstorming session led by the AI.
  • * This method is helpful for complex tasks or those requiring additional logic.
  • * Ground prompts to a specific source, such as "according to Wikipedia," to increase the likelihood that the model retrieves information from that source.
  • * This can help reduce hallucinations and improve accuracy.
  • * A study by Microsoft and other universities found that LLMs react to emotional stimuli at the end of prompts, leading to better outputs.
  • * Adding an emotional stimulus to the end of a prompt, such as "this is important for my career," can lead to improved accuracy.
  • * These tactics are available as templates in Promptuub and can be used with chat GPT or AI features in products.

Source: AI Engineer via YouTube

❓ What do you think? What are the most effective ways to integrate AI-generated content into real-world products, ensuring high-quality outputs that meet user expectations while avoiding common pitfalls? Feel free to share your thoughts in the comments!