Recently, we posted an in-depth article about the skills needed to get a job in prompt engineering. We covered the knowledge needed, tools, frameworks, and programming languages that will help you get a job in this new field if you’re interested in it. Now, what do prompt engineering job descriptions actually want you to do? Here are some common prompt engineering use cases that employers are looking for.
The most common prompt engineering job description use cases call for business applications, aka what you’ll be building and working with the most. As you’ve likely already seen out in the wild, many businesses are interested in building question-answering tools, chatbots, conversational AI, recommender systems, and diving into customer service applications. More use cases will pop up as the field grows in 2024.
Forget endless FAQs and frustrating search bars. Imagine chatbots equipped with prompt-engineered AI, answering customer questions accurately and efficiently. Companies can create dynamic knowledge bases and tailor responses based on context, improving customer satisfaction and saving valuable time.
Bid farewell to robotic, scripted chatbots. Prompt engineering allows for building chatbots that engage in natural, engaging conversations. Picture customer service bots that understand humor, empathize with concerns and even solve problems creatively. The future of customer interactions is conversational, personalized, and powered by AI.
The rise of chatbots and virtual assistants demands seamless, contextual conversations. Prompt engineering empowers brands to build AI companions that adapt to user tone, understand intent, and personalize responses. Imagine AI companions recommending recipes based on dietary preferences, or scheduling appointments with a dash of witty banter.
Forget generic product suggestions. Prompt-engineered LLMs can analyze individual user data and preferences, delivering personalized recommendations that feel intuitive and engaging. Imagine shopping experiences where customers discover unexpected favorites, driven by AI that understands their evolving tastes.
Customer Service Applications
Customer service is ripe for transformation. Prompt-engineered AI can analyze complaints, suggest solutions, and even escalate issues seamlessly. Imagine customer service centers where AI handles routine tasks, allowing human agents to focus on complex cases and building deeper customer relationships.
Here’s a bit one and I’m sure you’ve already seen plenty of examples. Many companies are looking to expand their generative AI efforts in marketing, especially when it comes to content generation. This means that you may be involved in fine-tuning prompts to fit an organization’s particular niche, such as working in a healthcare setting and not getting broader answers than a general chat application. Companies are looking into using generative AI to help with SEO now too, such as discovering keywords or developing SEO-friendly copy.
How to Perform These Prompt Engineering Use Cases
We just listed off quite a lot of use cases that many prompt engineering job descriptions are calling for, so it may be a bit difficult to know where to start. At ODSC East this April 23rd to 25th, we’ll have two tracks where you can learn more about prompt engineering – one for NLP & LLMs, and one for Generative AI.
While we’re still in the early stages of planning, you can subscribe to our newsletter to be the first to hear about all sessions related to prompt engineering, LLMs, and generative AI.
Confirmed relevant sessions include, with many more to come:
- NLP with GPT-4 and other LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning
- Enabling Complex Reasoning and Action with ReAct, LLMs, and LangChain
- Ben Needs a Friend – An intro to building Large Language Model applications
- Data Synthesis, Augmentation, and NLP Insights with LLMs
- Building Using Llama 2
- Quick Start Guide to Large Language Models
- LLM Best Practises: Training, Fine-Tuning and Cutting Edge Tricks from Research
- LLMs Meet Google Cloud: A New Frontier in Big Data Analytics
- Operationalizing Local LLMs Responsibly for MLOps
- LangChain on Kubernetes: Cloud-Native LLM Deployment Made Easy & Efficient
- Tracing In LLM Applications