[RECORDED] Knowledge and Prompt Engineering Part 2: Focus on Prompt Design Approaches

In our last session on knowledge and prompt engineering, we focused more on the knowledge design side. In this follow-up session, we will explore prompt approaches and practices more deeply.

We will cover the following topics:

  • Structured prompting, based on use cases, scenarios, and the RACE framework (Role, Action, Context, and Examples)
  • Situational factors that affect the prompt framework
    • Capability – Are you interacting with a chatbot or an assistant?
    • Model – Which LLM are you working with: OpenAI's GPT, Mixtral, Llama2, or another?
    • Use case – Do you need translation, generation, or a Q&A system?
    • Mode – Are you working with text, vision, or voice?
    • Action – Function calling or fine-tuning?
  • How the prompt design framework needs to align with and take signals from the knowledge engineering framework
  • Approaches to optimize your prompts and achieve the best responses from an LLM
    • Working through problems step-by-step using the Tree of Thought (ToT), chain of thought, or iterative prompting techniques
    • Injecting emotion to prompt emotionally engaging language
    • Including signals to improve LLM performance:
      • Corporate signals such as brand guidelines, terminology, and data sources
      • User signals from prior interactions
      • Industry-specific data


    • Seth Earley
      CEO and Founder, Earley Information Science
    • Nick Usborne
      Copywriter, Trainer, and Speaker 
    • Sanjay Mehta
      Principal Solution Architect, Earley Information Science


Meet the Author
Earley Information Science Team

We're passionate about managing data, content, and organizational knowledge. For 25 years, we've supported business outcomes by making information findable, usable, and valuable.