Shaping the horizon

Prompt engineering is the process of designing and refining prompts or instructions for machine learning models, particularly language models like GPT-3, to produce specific, desired outputs. This technique is often used to fine-tune the behaviour and output of the model in order to make it more useful and safe for various applications.

Prompt engineering involves various effective works:

1. Crafting effective prompts: Designing clear and concise prompts that convey the desired task or context to the model.

2. Iterative refinement: Engineers and researchers experiment with different prompts and adjust them based on the model's responses, aiming to improve the quality and relevance of the generated content.

3. Bias and safety mitigation: In the context of language models, prompt engineering can also be used to reduce biases and ensure safety by carefully wording prompts to avoid generating harmful or biased content.

4. Controlling output: By carefully crafting prompts, it's possible to guide the model's output towards specific formats, styles, or information, making it more suitable for particular applications.

5. Fine-tuning: In some cases, prompts are used in conjunction with fine-tuning, which involves training a model on specific datasets or tasks to make it more proficient in generating desired outputs.

6. Evaluation and testing: Prompt engineering also involves evaluating the model's responses to different prompts, assessing its performance, and making adjustments as needed.

The concept of prompt engineering can be traced back to the development and use of large language models, particularly the development of OpenAI's GPT (Generative Pre-trained Transformer) series. The idea of controlling and guiding the output of such models using prompts has evolved as these models have become more powerful and versatile. Researchers and developers have used prompts with earlier language models to get specific responses. However, it gained more prominence with the advent of GPT models. The GPT-1 model, released by OpenAI in 2018, was one of the early models in the GPT series. While it was a significant step forward in natural language generation, it had limitations in terms of controlling its output. When OpenAI eventually released GPT-2 in 2019, they demonstrated the importance of prompt engineering as a means of controlling the model's behaviour.

As GPT-2 and later iterations of GPT models raised concerns about bias, misinformation, and inappropriate content generation, prompt engineering became a key approach to mitigate these issues. With increasing awareness of the ethical and societal implications of AI models, organisations and researchers developed ethical guidelines and best practices for prompt engineering. Prompt engineering continues to evolve as researchers and developers work to improve the capabilities and safety of large language models.

As per the market knowledge update in September 2021, job openings related to prompt engineering in the field of generative AI and natural language processing were on the rise. However, please note that the job market is dynamic and can change rapidly, so it's essential to check current job listings on relevant job search websites, company career pages, and professional networking platforms for the most up-to-date opportunities. Here are some common job titles and roles one might find related to prompt engineering in generative AI:

1. Machine learning engineer: Machine learning engineers work on developing, training, and fine-tuning machine learning models, including language models, often using prompt engineering techniques.

2. NLP engineer: Natural language processing (NLP) engineers focus on building systems and applications that process and understand human language. They may utilise prompt engineering for controlling language models.

3. AI research scientist: Research scientists in the field of AI work on advancing the state of the art in generative AI, which can include exploring new techniques for prompt engineering.

4. Data scientist: Data scientists may be involved in creating and curating datasets for training and evaluating language models, which can be a critical component of prompt engineering.

5. Ethical AI researcher: Some organisations are looking for researchers who specialise in ethical AI, including developing guidelines and best practices for prompt engineering to mitigate issues related to bias and safety.

6. AI product manager: Product managers in AI-related roles work on defining the features, behaviour, and use cases of AI applications, often involving prompt engineering for customisation.

7. Research engineer: Research engineers are typically involved in experimenting with and developing new techniques for controlling and guiding the output of generative AI models, which may include prompt engineering.

8. AI software developer: AI software developers write the code that implements prompt engineering techniques and integrates AI models into applications.

9. AI ethicist: Ethicists specialising in AI and machine learning work on addressing the ethical challenges of prompt engineering, ensuring that the models produce responsible and unbiased output.

When searching for these roles, you can use keywords such as "prompt engineering," "generative AI," "NLP," "machine learning," and "language models" to find relevant job openings. Be sure to tailor your job search to your specific interests and expertise within the field of generative AI and prompt engineering. Additionally, consider exploring opportunities at research institutions, tech companies, AI startups, and organisations specialising in AI ethics and responsible AI development. In summary, while the concept of using prompts to guide language models existed prior to the GPT series, it gained significant attention and importance with the development of GPT models. Prompt engineering has since become a critical technique for fine-tuning the behaviour of these models, addressing ethical concerns, and making them more useful in various applications.

As companies look for strategies to support the training of and adaptation to more artificial intelligence systems, prompt engineers are one of the most in-demand professions. It aids in the large-scale implementation of relatively new language models, producing outcomes that frequently succeed and frequently fail. Our team came upon some very fascinating information when investigating this backdrop. Opportunities are available in the present for those who are familiar with and proficient in using AI tools. According to a survey published by LinkedIn, the number of postings tagged with "generative AI" has increased by nearly 36 times since it first appeared.

Additionally, compared to its number in 2021 and 2020, the number of employment under "GPT" increased by more than 51 per cent. Most of their jobs are open to everyone around and do not specifically target those with computer backgrounds. It is still too early for us to make any predictions about the future of quick engineering because it is unclear how far it will advance. However, a lot of businesses have begun hiring for this role. A machine learning expert who can improve the output of an AI tool and help the firm can earn up to USD 250,000 from Klarity, another AI startup that reviews documents.

Overall, prompt engineering is a critical aspect of working with machine learning models to ensure they generate content that aligns with the goals and requirements of a given application or task. It's particularly important when using large, general-purpose language models to tailor their behaviour to specific use cases.

The writer is Professor of Computer Science and Engineering, Sister Nivedita University. Views expressed are personal