Prompt Security
This tutorial focuses on two critical aspects of prompt engineering: preventing prompt injections and implementing content filters in prompts. These techniques are essential for maintaining the security and safety of AI-powered applications, especially when dealing with user-generated inputs.
As AI models become more powerful and widely used, ensuring their safe and secure operation is paramount. Prompt injections can lead to unexpected or malicious behavior, while lack of content filtering may result in inappropriate or harmful outputs. By mastering these techniques, developers can create more robust and trustworthy AI applications.
What you'll learn
- 1Prompt Injection Prevention: Techniques to safeguard against malicious attempts to manipulate AI responses.
- 2Content Filtering: Methods to ensure AI-generated content adheres to safety and appropriateness standards.
- 3OpenAI API: Utilizing OpenAI's language models for demonstrations.
- 4LangChain: Leveraging LangChain's tools for prompt engineering and safety measures.
About this tutorial
This hands-on Jupyter notebook is part of Prompt Engineering, a free open-source repository by Nir Diamant covering prompt engineering techniques with runnable code examples and detailed explanations.
Prompt Engineering: Zero to Hero
The expanded book version of this repo: 22 prompt-engineering techniques explained in depth, with hands-on exercises that take you from fundamentals to advanced steering.
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