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Prompt Engineering: Expert Tips for a variety of Platforms

 


Prompt engineering has become a crucial aspect of harnessing the full potential of AI language models. Both Google and Anthropic have recently released comprehensive guides to help users optimise their prompts for better interactions with their AI tools. What follows is a quick overview of tips drawn from these documents. And to think just a year ago there were countless YouTube videos that were promoting 'Prompt Engineering' as a job that could earn megabucks...

The main providers of these 'chatbots' will hopefully get rid of this problem, soon. Currently their interfaces are akin to 1970's command lines, we've seen a regression in UI. Constructing complex prompts should be relegated to Linux lovers. Just a word of caution, even excellent prompts don't stop LLM 'hallucinations'. They can be mitigated against by supplementing a LLM with a RAG, and perhaps by 'Memory Tuning' as suggested by Lamini (I've not tested this approach yet).  

In the meantime, here's how to cope with retro interfaces.

Clear and Direct Instructions

One of the most critical aspects of prompt engineering is to provide clear and direct instructions. This ensures that the AI model understands the task at hand and can generate relevant and coherent responses. For instance, when using Anthropic's Claude, it is essential to describe your task in detail, specifying exactly what you want the model to accomplish.


Separating Data from Instructions

Another key technique is to separate data from instructions. This helps the model to differentiate between the input data and the specific actions it needs to perform on that data. This approach is particularly useful when working with complex tasks that require multiple steps[4].


Using XML Tags

XML tags can be used to mark different parts of your prompt, which helps the AI model to recognize the structure and organization of the input. This is especially useful when dealing with tasks that require specific formatting or identification of certain elements within the text.

Including examples in your prompt can significantly improve the model's performance. The more examples you provide, the better the model can understand the task and generate relevant outputs. This approach is particularly effective when working with tasks that require specific formats or styles


Long Context

Using long context can be beneficial when working with tasks that require a deeper understanding of the input. Anthropic's Claude, for example, can process up to 100,000 tokens, allowing for more detailed and nuanced prompts. The average prompt is, apparently only 9 words long, 21 words is considered to be the minimum that's sufficient.


Avoiding Hallucinations

Hallucinations occur when the AI model generates information that is not present in the input data. To avoid this, it is essential to ensure that your prompts are specific and well-defined, and that you provide enough context for the model to understand the task accurately.


Building Complex Prompts

Building complex prompts involves combining multiple techniques and approaches to create a comprehensive and detailed prompt. This can be particularly useful when working with tasks that require multiple steps or involve complex data analysis.


Experimentation and Refining

Finally, it is crucial to experiment with different prompts and refine them based on the model's responses. This iterative process helps to identify the most effective prompts and optimize them for better performance.



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