Prompt Engineering, also known as In-Context Prompting, refers to methods for how communicating with LLM to steer its behavior for desired outcomes without updating the model weights.
Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs).
Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools.
Another important consideration in prompt engineering is fine-tuning the prompts. This involves tweaking the wording and structure of the prompts to optimize their effectiveness. The goal is to create prompts that are clear, concise, and unambiguous, while also providing the model with enough information to generate accurate and relevant responses.
To be effective, prompt engineering requires a deep understanding of the problem domain and the language used by the target audience. It also involves extensive testing and experimentation to refine the prompts and improve the model's performance. If you can efficiently convey instructions, you might thrive in this industry.
There are several techniques and tools that can be used to facilitate prompt engineering. For example, some NLP frameworks provide built-in tools for generating prompts and evaluating their effectiveness. Other techniques, such as human evaluation and crowdsourcing, can also be used to gather feedback on the effectiveness of different prompts.
Overall, prompt engineering is an essential component of NLP model development, as it helps to ensure that the model is accurate, reliable, and effective at generating high-quality responses to user input. By carefully designing and optimizing the prompts used by the model, developers can improve its performance and enhance its ability to understand and respond to natural language inputs.
It’s part of a dramatic increase in demand for workers who understand and can work with AI tools. According to LinkedIn data shared with TIME, the number of posts referring to “generative AI” has increased 36-fold in comparison to last year, and the number of job postings containing “GPT” rose by 51% between 2021 and 2022. Some of these job postings are being targeted at anyone, even those without a background in computer science or tech.
It’s too soon to tell how big prompt engineering will become, but a range of companies and industries are beginning to recruit for these positions. Anthropic, a Google-backed AI startup, is advertising salaries up to $335,000 for a “Prompt Engineer and Librarian” in San Francisco. Applicants must “have a creative hacker spirit and love solving puzzles,” the listing states.
Automated document reviewer Klarity is offering as much as $230,000 for a machine learning engineer who can “prompt and understand how to produce the best output” from AI tools. Some of these jobs can even pay up to $335,000 a year.
With the rise in generative artificial intelligence, a host of companies are now looking to hire “prompt engineers” who are tasked with training the emerging crop of AI tools to deliver more accurate and relevant responses to the questions real people are likely to pose.