Google has released a guide on creating effective prompts for neural networks

What is prompt engineering

Creating high-quality prompts has become an essential skill since the rise of generative AI. Clearly formulated instructions allow for more accurate and relevant results from neural networks.

In the document, Google explains that the quality of the response depends on the structure of the prompt, the model used, configuration, word choice, tone, and context.

Key principles of prompt creation

Google highlights 10 key recommendations for working with LLMs:

1. Provide examples — at least one or several, to help the model recognize patterns.
   
2. Formulate simply — avoid complex structures and unnecessary information.
   
3. Be specific — clarify details using contextual and system prompts.
   
4. Use instructions instead of restrictions — clearly explain what should be achieved.
   
5. Control the length of the response — set a limit on the number of tokens.
   
6. Use variables — store information in variables to avoid repetition.
   
7. Experiment with writing styles — try different formats and styles of requests.
   
8. Mix answer classes — introduce variety in examples for classification.
   
9. Adapt to model updates — consider new versions and capabilities of LLMs.
   
10. Work with output formats — for example, receive responses in JSON format for data processing.

As technology advances, prompt engineering becomes a critical skill for working with neural networks. The better the user formulates the request, the more accurate and useful the AI's response will be.

These tips will help you work more effectively with AI and get more precise answers. Google's guide is available for free and can be useful for developers, researchers, and anyone who wants to interact effectively with artificial intelligence.