Skip to main content

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.



Labels:

Comments

Post a Comment

Popular posts from this blog

OpenAI's NSA Appointment Raises Alarming Surveillance Concerns

  The recent appointment of General Paul Nakasone, former head of the National Security Agency (NSA), to OpenAI's board of directors has sparked widespread outrage and concern among privacy advocates and tech enthusiasts alike. Nakasone, who led the NSA from 2018 to 2023, will join OpenAI's Safety and Security Committee, tasked with enhancing AI's role in cybersecurity. However, this move has raised significant red flags, particularly given the NSA's history of mass surveillance and data collection without warrants. Critics, including Edward Snowden, have voiced their concerns that OpenAI's AI capabilities could be leveraged to strengthen the NSA's snooping network, further eroding individual privacy. Snowden has gone so far as to label the appointment a "willful, calculated betrayal of the rights of every person on Earth." The tech community is rightly alarmed, with many drawing parallels to dystopian fiction. The move has also raised questions about ...
Post a Comment
Read more

What is happening inside of the black box?

  Neel Nanda is involved in Mechanistic Interpretability research at DeepMind, formerly of AnthropicAI, what's fascinating about the research conducted by Nanda is he gets to peer into the Black Box to figure out how different types of AI models work. Anyone concerned with AI should understand how important this is. In this video Nanda discusses some of his findings, including 'induction heads', which turn out to have some vital properties.  Induction heads are a type of attention head that allows a language model to learn long-range dependencies in text. They do this by using a simple algorithm to complete token sequences like [A][B] ... [A] -> [B]. For example, if a model is given the sequence "The cat sat on the mat," it can use induction heads to predict that the word "mat" will be followed by the word "the". Induction heads were first discovered in 2022 by a team of researchers at OpenAI. They found that induction heads were present in ...
Post a Comment
Read more