Prompt Engineering

Intermediate

Prompt Engineering (Intermediate)

This level focuses on advanced techniques and strategies for prompt engineering, which are essential for creating sophisticated and effective prompts, managing AI model responses, and minimising errors. The goal is to master the art of guiding AI models effectively by combining advanced prompting strategies with model parameter tuning to achieve accurate and context-aware outputs.

  1. Chaining Techniques
    • Description: Use multi-step prompts to break down complex tasks or guide logical reasoning step-by-step.
    • Reason: Decomposing tasks helps improve the accuracy and coherence of responses.
    • Example Task: Create a series of prompts to guide the model through analysing data and generating a summary.
  2. Using Prompt Templates
    • Description: Create templates by separating the fixed structure of a prompt from variable user inputs.
    • Reason: Templates improve consistency, efficiency, and scalability in prompt design.
    • Example Task: Design a template for generating meeting summaries with placeholders for details like agenda, participants, and key outcomes.
  3. Profiling Model Responses / Speaking for the Model
    • Description: Pre-fill parts of the model’s response to enforce a desired output format.
    • Reason: Prefilling ensures adherence to specific formats, such as JSON or XML.
    • Example Task: Write a prompt that begins the response with a JSON structure for user data.
  4. Minimizing Hallucinations
    • Description: Use techniques to reduce the likelihood of incorrect or fabricated responses.
    • Reason: Ensuring reliability and factual accuracy in the model’s outputs is crucial.
    • Example Task: Write a prompt instructing the model to acknowledge when it doesn’t know an answer and another requiring evidence-based responses.​​​​​​​