Prompt engineering is one of the most important skills every stakeholder in AI needs to develop, most especially in the case of NLP. This construct for guided behavior formulates Q/A statements that AI will then provide appropriate and compatible answers to. Armed with this knowledge, here are some online courses and tutorials you can learn from so you can design your own toolkit:
One of the most unorthodox yet entertaining ways to get acquainted with prompt engineering is through online courses. There are many AI and NLP courses on Coursera, Udemy, or edX. They will include hands-on exercises that will walk you through, step-by-step, how to build useful prompts. And if that's not enough, services like Fast. You'll find tutorials for newbies targeted at those who are new to Hugging Face and more advanced users here.
Books and research papers on prompt engineering. Some good titles are "Deep Learning for Natural Language Processing" by Palash Goyal and ("Natural Language Processing with Transformers: Interpret, Understand Text and Generate Creative Content " by Lewis Tunstall; Leandro von Werra; Thomas Wolf.). These will go into the underlying principles of NLP as well as use cases for prompt engineering. The research papers by arXiv is a good web source, so please check them for what the latest trend or methods are.
Prompt libraries can really be a godsend when you need to save time and come up with inspiration. Tools like OpenAI's GPT-3 Playground and Hugging Face's Model Hub give us the power to try and understand what the AI will respond if given a prompt. This method, however, only gives us an idea of the kind of output it might produce. Several prompt libraries have contributions from the community, which is an excellent way to learn by example. This way you can identify what works through such examples.
The second critical need for instant engineering is APIs to existing AI models. That's the interface, and includes a few API-first platforms: OpenAI, Cohere, and AI21 Labs, which let you test different models through their APIs. These APIs will let you live test your prompts and change them based on answers from the AI. Research their documentation-there's great information on what a given model does and doesn't do.
After planning out your prompts, you also need to decide whether they are actually effective or not. It isn't particularly clear exactly how great your prompts could really be, but you can see your prompts with some incredible tools like the AI Dungeon Prompt Analyzer. They will run through your response data and give some changes, which again could help further the quality of your prompts. You will also partially be roasted by the users when they correctly tear into your product. Reddit, Stack Overflow, and some others of similar platforms where you can engage with these communities to perhaps even get the most fine-tuned set of suggestions on how to improve your prompts.
Engage yourself with some community forums and group discussions that can add one more dimension to the learning process. You can use platforms like Discord, Slack, and specialized forums which help you get in touch with other prompt engineers and enthusiasts related to AI. Read articles, ask questions, comment on experiences and other problems of the trade-you get to know a lot of views and suggestions.
Experimentation is the best learning resource. You never have to be afraid of trying and failing in fear of what the results might be. As their feedback and model responses begin to come in, you can gradually refine your prompts.
Over time, people who play with partial-sleep plans find that they're testing and tracking in a way that's almost like a notebook of experiments: What works, what doesn't work and which ad-hoc innovation seemed to deliver even greater gains.
You need mature prompt engineering toolkit which combines education and hands-on experience with community service. There is a lot to learn as well as improve effectiveness in prompt writing by taking online courses, reading materials, downloads from libraries of prompts, and API calls for evaluation and communities' forums. As a productivity engineer you are always on the path toward prompt engineering and you will increasingly do better using AI the more you practice and engage with your human friends.
