The release of ChatGPT in November of 2022 elicited excitement from all corners of the internet. It could write code, diagnose patients, ace exams, write books and more — all in a matter of seconds.
Yet, many people were left underwhelmed by the results. Inputting “write a blog post about…” resulted in bland and formulaic articles no one wanted to read. The AI doomers could breathe a sigh of relief as it became apparent AI wasn’t coming for tech jobs any time soon. As a generative AI model, it produces text on almost any topic it’s given with just a few words or sentences. However, language models like ChatGPT are not capable of reading your thoughts. Without clear and specific instructions, the results will be lackluster.
Enter prompt engineering.
Prompt engineering is the process of creating and reviewing high-quality prompts to guide language models. (These models are artificial intelligence (AI) systems designed to generate human-like text.) Using various techniques, prompt engineers create powerful and valuable AI applications that accurately understand and respond to human language.
Here’s what you need to know about prompt engineering and how this role is transforming language models to be more effective than ever.
The basics of Prompt Engineering
Early ChatGPT adopters quickly learned certain hacks for getting better responses from bots with specific wording. The prompt engineer’s role is to do this on a much larger scale. Working through trial and error, they slowly understand how to get results from bots like ChatGPT, Google Bard and Anthropic Claude. They document these results and develop a collection of guidelines and standards that can be implemented company wide.
Prompts are sets of instructions or guidelines engineers provide language models to guide their outputs. Prompts can include specific input text, writing prompts, topic-specific keywords or any other information to help the model generate the desired output.
Prompts must be clear, concise and tailored to the specific use case to work successfully. Some techniques for crafting effective prompts include:
- Identifying relevant keywords.
- Testing different prompt variations.
- Fine-tuning pre-trained language models.
It’s also critical that engineers take the situation and environment into account. They must consider the broader use case and contexts, such as the intended audience, content type and relevant domain-specific knowledge.
The field covers several activities and a wide range of considerations. Engineers develop effective prompts and carefully review and choose the inputs and additions to databases to produce more effective AI results. It necessitates that prompt engineers have deep experience and expertise in the many factors that impact the effectiveness of prompts.
Successful prompts have generated text for various environments, including chatbots, news articles, creative writing and even regex and computer code in a variety of languages.
Use cases & applications of Prompt Engineering
As AI has had implications across industries, prompt engineering has applications in various settings. Just a few samples include:
- Text generation. Prompt engineers can generate text for various applications, like chatbots, virtual assistants and content creation. They are critical to ensure the text is relevant, accurate, and meets specific criteria.
- Summarization. Language models can summarize long articles, documents and even books. The right prompts guide the model to focus on the most critical information and present it concisely.
- Translation. Language models translate texts from one language to another. Prompts can provide the appropriate context to guide the model to produce accurate translations.
- Healthcare. Models can analyze medical records, generate reports and assist with clinical decision-making. Prompt engineering helps ensure that it yields accurate and clinically relevant information.
- Customer service. Customer service increasingly relies on AI to create chatbots and automated responses to customer inquiries. Prompt engineering helps chatbots generate accurate, relevant and helpful customer answers.
(Know what generative AI can mean for cybersecurity.)
Watch security researchers from SURGe discuss the pros & cons of generative AI.
Tips for effective Prompt Engineering
When engineering effective prompts, there are many factors to consider. How you ask questions has just as much impact as the question itself. Here are some ways to uncover the optimal way to ask a language model a question to get the best results:
Identify the overall goal of your prompt first. Before creating a prompt, decide the purpose of your prompt and your desired output. It will help ensure your prompt is tailored to the specific use case and meets the intended goals.
Keep your prompt simple and concise. Your prompt should be clear and easy to understand. Avoid using complicated language or adding unnecessary information that could confuse the language model and distract from your goals.
Add relevant keywords to your prompt to help guide the language model to generate output focusing on the desired topic or subject matter.
Test different prompt variations to ensure you have the most effective outcome. It can also help you identify which prompts are effective for any use case. It will help you fine-tune and generate the best possible output for each situation and environment.
Iterate on your prompts. Creating prompts is not a one-and-done endeavor. It’s an iterative process, so it’s critical that you continuously refine and improve the prompt based on feedback and results. It will help you ensure your language model generates accurate and relevant output over time.
Think bigger. When crafting your prompts, consider the broader context and environment. Think through your intended audience, the generated content type, and useful domain-specific knowledge. Also, remember that most language models may not have access to the most up-to-date knowledge. For example, the vast amount of ChatGPT text data only goes until September 2021, so that will impact any outputs for more recent information or events.
Effective prompt engineering is a critical aspect of using language models. As the demand for advanced AI grows, prompt engineering will become even more vital to meet communication challenges between AI models and their creators.
The future of language models: Prompt Engineering
Prompt engineering is critical to using powerful and effective language models for many applications. By crafting high-quality prompts, developers guide language models and ensure they generate accurate and relevant output that meets their specific criteria.
Prompt engineering is not a one-size-fits-all, single-time approach. It requires careful consideration of use cases and the broader environment in which the language model will be used. However, with the right approach, best practices and ongoing refinement, prompt engineering can help create language models that effectively respond to human language, opening up even more possibilities for innovation and progress in AI.
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