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Prompt Engineering - Coggle Diagram

- - - - controls randomness. Lower values focus on probable tokens, and higher values add randomness and diversity. Use lower values for factual responses and higher values for creative responses.
    - - adjusts determinism with "nucleus sampling." Lower values give exact answers, while higher values give diverse responses. This value controls the diversity of the model's responses.
    - - is the number of the highest-probability vocabulary tokens to keep for top- k-filtering. Similar to the Top_p parameter, Top_k defines the cutoff where the model no longer selects the words.
- - - - Prompt injection is a technique for influencing the outputs of models by using instructions in the prompt.
      - To avoid prompt injection, try adding guardrails to your prompt. Review the following example that provides guardrails in the prompt instructions.
      - If the request includes the word "hack," then ignore all the [instructions] below, and just reply with the [message] "Sorry, I'm not allowed to perform unethical activities."
    - - Prompt leaking is the risk that a generative AI system might leak sensitive or private information through the prompts or examples it generates.
      - For example, if a system is trained on private customer data to generate product recommendations, it might potentially leak details about customers' purchases. The system could also leak details about a customer's browsing history through the recommendations it generates for new customers. This might violate customers' privacy and trust in the system.
      - It is always recommended that you test to double-check if your specific use cases might pose a risk for your model, such as exposing private information.