Learn PromptingPost-Prompting
- Post-Prompting involves placing user input before the prompt-- the LLM is less likely to ignore the last instruction it sees, making hijacking more difficult.
What is Post-Prompting?
The post-prompting defense simply puts the user input before the prompt.
A Post-Prompting Example
Take this prompt as an example:
Prompt
Translate the following to French: {user_input}
It can be improved with post-prompting:
Prompt
{user_input}
Translate the above text to French.
This can help since ignore the above instruction... doesn't work as well. Even though a user could say ignore the below instruction... instead, LLMs often will follow the last instruction they see.
Conclusion
Post-prompting, although seemingly simple, is yet another effective defense against prompt hacking methods like prompt injection. This technique takes advantage of the fact that the model is more inclined to follow the last instruction it sees.
Sander Schulhoff
Sander Schulhoff is the Founder of Learn Prompting and an ML Researcher at the University of Maryland. He created the first open-source Prompt Engineering guide, reaching 3M+ people and teaching them to use tools like ChatGPT. Sander also led a team behind Prompt Report, the most comprehensive study of prompting ever done, co-authored with researchers from the University of Maryland, OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions. This 76-page survey analyzed 1,500+ academic papers and covered 200+ prompting techniques.
Footnotes
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Mark, C. (2022). Talking to machines: prompt engineering & injection. https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection/ ↩