Since OpenAI’s ChatGPT kicked down the door and brought large language models into the public imagination, being able to fully utilize these AI models has quickly become a much sought-after skill. With that said, companies are now realizing that to bring out the full potential of AI, prompt engineering is a must. So we have to ask, what kind of job now and in the future will use prompt engineering as part of its core skill set?
Let’s take a look at a few jobs that will be using this emerging skill, and what it might mean for the future of your field.
AI Prompt Engineer
An AI Prompt Engineer is a specialized professional at the forefront of the AI and NLP landscape. For those who might not know, this role acts as a bridge between human intent and machine understanding, shaping the interactions we have with AI systems. AI Prompt Engineers are responsible for crafting and refining the prompts or queries that users input to AI models. They possess a deep understanding of language nuances, context, and domain-specific vocabulary.
With expertise in both linguistics and data science, they design prompts that extract accurate and relevant responses from AI models, ensuring that the generated content aligns with user expectations and industry standards. Though this is one of the newest job titles to come into being, it is becoming clear that AI Prompt Engineers are poised to become increasingly pivotal. As AI models become more sophisticated and versatile, the demand for tailored, context-aware interactions grows. They will also play a vital role in fine-tuning and optimizing these models for various industries and applications.
Natural Language Processing Engineer
Natural Language Processing Engineers who specialize in prompt engineering are linguistic architects when it comes to AI communication. Where their expertise lies in the ability to craft input instructions that guide AI models such as GPT-4 so that these LLMs can produce accurate and contextually relevant outputs. Often, NLP engineers who specialize in prompt engineering will work closely with domain experts, where they create prompts that extract insights, support decision-making, and ensure responsible AI interactions.
What makes NLP Engineers unique as part of the AI team is that they are pivotal in fine-tuning prompt strategies, reducing biases, and advancing human-AI dialogue. They streamline prompt development, shaping how AI responds to users across industries. As NLP integration expands, these specialists play a crucial role in making interactions more seamless, effective, and insightful, steering the future of human-AI communication.
Machine Learning Engineer
A Machine Learning Engineer who specializes in prompt engineering is a catalyst for effective AI communication. They excel in designing input prompts that guide AI models like GPT-4 and other LLMs to generate accurate and relevant outputs that are specific to the task. Collaborating with domain experts, they craft prompts that extract insights, enabling informed decision-making and responsible AI interactions. Often, they will work alongside Prompt Engineers and NLP Engineers to refine prompting strategies and expectations.
As industries begin to scale and learn how to fully utilize the power of AI, it’s likely that more and more machine learning engineers will work closely to further refine prompt strategies, curbing biases and advancing human-AI conversations. As these teams work together to optimize prompt development to shape AI responses across industries, they will also work with the algorithms that power LLMs to ensure that any integration of AI continues on proper trajectories while ensuring that human-AI engagement maintains proper ethical standards.
If a Data Scientist is able to add prompt engineering into their toolkit, they can find themselves as effective AI communicator. How they can make this happen by excelling in designing input prompts that guide AI models like GPT-4 and/or other domain-specific LLMs in order to generate accurate and relevant outputs. Often this can be seen in delivering greater data insights to stakeholders, optimizing workflows, and providing methods of extracting data that were not possible before.
Product Managers that specialize in prompt engineering will see themselves collaborating with AI experts and stakeholders to design input prompts that steer AI models like GPT-4 to produce precise and contextually relevant responses. For example, this can include product managers in the e-commerce space creating prompts that allow them to solicit personalized product recommendations for A/B tests. This can also be applied to other industry-specific use cases and requirements, such as in healthcare where prompts can help create greater diagnostic insights related to their products.
Clearly, AI will continue to scale across almost every industry and vertical, and product managers who are apple to utilize prompt engineering will find themselves at the forefront of AI integration and working with new entities in the job market such as prompt engineers. The goal, of course, using AI and AI-powred capabilities to ensure that there is a seamless alignment of overall product vision.
Just like with any new technology that is introduced, AI has spawned a new job, Prompt Engineer, and with it, multiple pre-existing jobs are likely to be at the forefront of using prompt engineering to enhance their overall productivity. Though with each the use can use cases could be drastically different, the one thing that they do have in common is that with proper prompt engineering skills, data scientists, machine learning engineers, product managers, and others can find themselves super-powered by AI, enhancing their pre-existing skills and opening new doors that didn’t exist in the past.
Now if you want to take your prompt engineering skills to the next level, or want to learn the basics, then you don’t want to miss ODSC West 2023. At ODSC West, you’ll experience multiple tracks with Large Language Models, having its own track. With a full track devoted to NLP and LLMs, you’ll enjoy talks, sessions, events, and more that squarely focus on this fast-paced field.
Confirmed sessions include:
- Personalizing LLMs with a Feature Store
- Understanding the Landscape of Large Models
- Building LLM-powered Knowledge Workers over Your Data with LlamaIndex
- General and Efficient Self-supervised Learning with data2vec
- Towards Explainable and Language-Agnostic LLMs
- Fine-tuning LLMs on Slack Messages
- Beyond Demos and Prototypes: How to Build Production-Ready Applications Using Open-Source LLMs
- Automating Business Processes Using LangChain
- Connecting Large Language Models – Common pitfalls & challenges
What are you waiting for? Get your pass today!