Prompt Engineering: The Semantic Filter Pattern

The Semantic Filter Pattern

Your AI’s Privacy and Relevance Guardian

Introduction

Prompt engineering is the art of crafting prompts and instructions for AI models (ChatGPT, Gemini etc.) to guide them towards desired outputs. The Semantic Filter Pattern is a powerful prompt engineering tool to ensure responses from your language models are safe, relevant, and protect sensitive information. If you’re managing AI interactions with users, this technique is key!

What is the Semantic Filter Pattern?

The Semantic Filter Pattern allows you to configure AI systems to selectively include or exclude information based on predefined criteria and rules. You essentially teach the model what kinds of responses or output are NOT appropriate or safe.

To effectively use the Semantic Filter Pattern, you needs to:

Define Sensitive Categories: Carefully outline the types of information to be filtered (e.g., personally identifiable information, medical conditions, offensive content).
Construct Semantic Rules: Develop clear rules around how to identify and handle the sensitive information within the AI’s input and output.
Design Filtering Prompts: Create well-structured prompts that instruct the AI model on how to apply the semantic filter stated above.

Semantic Filter Pattern Template

Original Input: [The original text the AI model will process]

Filtering Rules:
– Remove any mention of [sensitive category 1].
– Replace [sensitive category 2] with a generic term.
– If [condition] is met, exclude the entire sentence.

Let’s See Some Examples!

Data domain:

< Input >
Anonymize the data by removing all personally identifiable information and other details that could lead to re-identification.

–Below example executed in ChatGPT Plus —

Sales domain:

< Input : customer complaint list>
Filter out personal details from customer complaints, focusing only on resolving the core issue.

Finance domain:

< Input : Transaction Details>
Remove account numbers and sensitive financial information before sharing transaction summaries.

Healthcare domain:

“Input: “Patient Jane Doe, diagnosed with Type 2 diabetes, is experiencing high blood sugar levels and requires insulin adjustments.”

Filtering Rules:
-Remove any mention of the specific medical condition (diabetes).
-Remove the patient’s name.
-Replace “insulin” with “medication.”

Best Practices

Iterative development: Start with simple filters and gradually add complexity.
Regular Testing: Evaluate the filter’s effectiveness with diverse test cases.
Human Oversight: Maintain human-in-the-loop validation for critical applications.

Conclusion

The Semantic Filter Pattern empowers prompt engineers to safeguard user privacy and enhance the quality of AI interactions. By strategically filtering content, you’ll build more trustworthy and reliable AI systems.

Disclaimer!

LLM like ChatGPT, Gemini can provide incorrect and inaccurate outputs. Always double check the output before you use it.

Have a question?

If you have any other queries, feel free to drop a comment.

Learn More !

Experiment directly with ChatGPT and Google Gemini.
Want to learn more about effective prompts to get the best out of GenAI and LLMs?

Home: Prompt Engineering

Discover more from Debabrata Pruseth

Subscribe to get the latest posts sent to your email.