Prompt Engineering
Generative AI

Prompt Engineering

Prompt engineering is a new field focussed on designing and refining transformer-based LLMs with specific text prompts to guide them for the most accurate outcomes.

What is Prompt Engineering? 

Prompt engineering is a relatively new field that combines human ingenuity with the power of large language models. Refining instructions and harnessing these models' capabilities unlock transformative interactions, personalized communication, and limitless creativity.

It is a maturing field focussed on designing and refining transformer-based large language models (LLMs) with specific text prompts to guide them for the most accurate outcomes.

Great prompts help harness the capabilities of LLMs at best. Prompt engineering involves creating clear and specific instructions so that models can process them to produce the most relevant output. 

Some exemplary implementations are Google's Smart Reply Feature and Open AI's GPT3 model for content creation. 

Isa Fulford and Andrew Ng highlight these two critical principles for prompt engineering:

  • Provide clear and specific instructions to help the model understand the task precisely and produce relevant output. 
  • Allow the model to think within a reasonable time frame. It helps the model better understand, process and interpret the instruction to drive the correct output.

Why Is Prompt Engineering Critical? 

By considering the complexities associated with LLMs and their tendency to hallucinate, carefully crafted prompts help avoid unwanted disruptions while allowing you to take the most out of LLM capabilities. Properly designed prompts empower users to perform a handful of tasks–create content, summarize text, extract information, computation, code generation, understand the sentiment, and more. 

Following points summarize the need for prompt engineering: 

  • Get relevant, accurate, and improved outcomes from models
  • Boost model efficiency for specific tasks without many trials and retraining 
  • Improve model performance as closely as humans by offering them the best exposure through instructions
  • To identify the limitations of LLMs 
  • Enable domain-specific knowledge transfer to LLMs for critical fields such as healthcare and fintech 

Techniques Used In Prompt Engineering 

ML practitioners use a bunch of techniques for prompt engineering. These prompting methods address aspects such as communicating with models, driving required output, and leveraging LLMs at their best. Some of these methods include: 

Zero-shot prompting

This straightforward technique generates output without providing additional examples. E.g., Sentiment analysis, where the model determines the sentiment of the given paragraph without any pre-training. 

Few-shot prompting

This technique uses limited examples to guide models to predict the desired output. For example, ‘create a Linkedin post on generative AI’ is the first instruction, followed by ‘enhance hook and add particular CTA’.

Chain-of-thought(CoT) prompting

The method designed by Google researchers helps dissect complex problems into manageable subproblems. CoT prompting comes with new adaptations such as self-consistency, Least-to-most, active, and multimodal prompting.

Generated knowledge prompting

This approach leverages the potential of language models to produce introductory information on complex topics. The generated information is used in the next pass to create more relevant content.

Directional stimulus prompting

This advanced method enables users to direct models in a specific manner. For example, you can ask to summarize the paragraph in 100 words or add particular seed keywords.

Summary of Prompting Techniques

Summary of Prompting Techniques, Author Image

Further Reading

Building LLM applications for production

AI Prompt Engineering Isn’t the Future

The Art of Prompt Engineering: Decoding ChatGPT