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Beyond basic prompting: Advanced techniques for harnessing the power of LLMs

Posted on 11-20-2023 05:06 PM
and filed in cloud-community-blog
NareshJasotani
Staff

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Imagine what you could do with a tool that can give informative answers to your questions, author a range of creative content, and translate languages. With large language models (LLMs), you can do all of this and more—as long as you know how. While basic prompting techniques can handle simple requests, they often fall short when it comes to complex tasks.

As part of our Gen AI Bootcamp series, this blog post will explore advanced prompt engineering techniques that can help you get the most out of LLMs. 

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Limitations of basic prompting techniques

Basic prompting techniques typically involve providing the LLM with a brief instruction or example of the desired output. For example, if you want the LLM to generate a poem, you might provide it with the prompt "Write a poem about love." While this type of prompt can be effective for eliciting simple outputs, it is often insufficient for more complex tasks.

For example, if you want the LLM to generate a persuasive essay on a particular topic, a basic prompt such as "Write a persuasive essay about gun control" is unlikely to be sufficient. You’ll need to provide more information about the topic, such as the different arguments for and against gun control, as well as the target audience for the essay.

Advanced prompt engineering techniques

There are a number of advanced prompt engineering techniques you can use to overcome the limitations of basic prompting techniques, including:

  • Chain-of-thought prompting: This technique breaks down a complex task into a series of smaller, more manageable subtasks. The LLM is then prompted to complete each subtask in turn, using the output of each subtask as input for the next subtask.
  • Few-shot prompting: This technique provides the LLM with a few examples of the desired output. The LLM can then use these examples to learn the patterns and parameters that govern the desired output.
  • Constraint-based prompting: This technique restricts the LLM with a set of constraints that must be met by the output. For example, you might constrain the LLM to generate an output that is a certain length or uses a particular vocabulary.
  • Meta-prompting: This technique prompts the LLM to generate prompts for itself. This can be a useful way to explore the LLM's capabilities and to generate more creative and interesting outputs.

These advanced prompt engineering techniques can be solve a wide range of real-world problems. For example, you can use chain-of-thought prompting to generate complex creative text formats, such as scripts, musical pieces, email, or letters, while few-shot prompting can train LLMs on new tasks without the need for large amounts of data.

Common mistakes to avoid

When creating prompts for LLMs, it is important to avoid a number of common mistakes. Some of these mistakes include:

  • Being too vague: Prompts should be as specific as possible. The more information you provide the LLM, the more likely it is to generate the desired output.
  • Making assumptions about the LLM's knowledge: Do not assume that the LLM knows what you mean. Be explicit about the desired output and provide any necessary background information.
  • Using jargon or technical language: Avoid using jargon or technical language that the LLM may not understand. Use plain language that is easy to understand.
  • Not providing feedback: If you are not satisfied with the LLM's output, provide feedback so that it can improve. Feedback can be in the form of corrections, suggestions, or examples.

As LLMs continue to develop, it is likely that even more advanced prompt engineering techniques will emerge. By staying up-to-date on the latest developments in prompt engineering, you can ensure that you are using the most effective techniques to achieve your desired results.

Want to learn more? Check out the full Gen AI Bootcamp workshop on-demand now!

Have questions? Please leave a comment below. 

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