Technical writers are primarily responsible for writing technical content. They need to document products that might be hard for users, especially technology. For that purpose, many hours may be spent wrestling with language and brainstorming ideas.
Large language models, and natural language processing, is starting to change all that. Artificial Intelligence is molding the way we write content and, more specifically, document products. Prompt engineers who are also technical writers stand a better chance of withstanding the tidal wave of disruption that AI may be bringing.
As one of the hottest tech jobs around, prompt engineers can benefit from the number of jobs on LinkedIn containing “GPT” (a popular generative AI tool) rising by more than half between 2021 and 2022.
The fact is, the better we can handle AI means technical writers are able to move with the times and make their jobs easier as well. Learning prompt engineering opens up a wealth of job opportunities that don’t require a computer science degree to be successful, but build on existing technical writing capabilities.
Understanding Prompt Engineering
The term prompt engineer may be slightly misleading, and some experts in the field have argued for “prompt specialist”, though this has not caught on yet. If you are a prompt engineer, you will be working with writing text-based prompts to feed into AI tools (such as ChatGPT) to create specific and accurate outputs.
Prompt engineers are concerned with creating content for technical products, working with complex information to make it clear and easy to understand. Writing is still vitally important, but prompt engineers use AI to create technical documents that would otherwise be more difficult to achieve.
Prompt engineers are likely to work in healthcare, technology, and engineering, just like more traditional technical writers. The emphasis is on crafting the prompts to control the output, which requires an understanding of AI models to ensure technical writers are inputting the right prompts.
Technical writers who work with prompt engineering must be able to teach the AI to learn from the prompts and become more accurate.
Essential Skills and Background for Prompt Engineering
There are a few specific skills that technical writers will require if they are to succeed in prompt engineering.
Language Model Knowledge
Prompt engineers need to have a knowledge and understanding of language models to be able to craft their prompts to generate specific text outputs. They don’t necessarily need to understand the mechanics behind the model but simply the principles governing it so they can effectively utilize it to create engaging and human-sounding technical documentation.
Programming Language Knowledge
Knowledge of programming language is not essential, but it certainly helps when it comes to prompt engineering. Python is a popular language that is used with prompt engineering to interact with APIs and manipulate data in order to make prompts more effective and accurate. Python works particularly well with ChatGPT to create prompts that generate highly specific outputs, and luckily many technical writers are already familiar with Python.
Hands-On Experience with AI and ML Technologies
You absolutely must have experience with AI and ML technologies, such as ChatGPT, published by OpenAI in 2022. Being able to work with and manipulate these technologies is a crucial part of the role of prompt engineers who want to create technical documentation. Since prompt engineering deals directly with AI, technical writers must seek out projects that use these technologies to become successful prompt engineers.
Understanding the Basics of Content Creation
The technical content created by AI tools due to prompts is unlikely to be the final product, especially since documentation must conform to specific standards and technical specifications. Working documentation still must be tested and approved before it can be released to customers or users, and prompt engineers must understand the principles of content creation to be successful. Content cannot be divorced from its human creators and rather supplemented by AI.
Ability to Work within Limited Timeframes
Even with the awesome power of AI behind you, you’ll still need to work within limited timeframes to create content to a deadline. Products can’t be shipped without appropriate documentation, and you’ll often be working just behind the pace of the engineering team. Since LLMs will be so integral to the process, project leads may expect even tighter deadlines than they would if you were simply creating documentation manually.
Types and Examples of Prompts
Now we’re going to look at a few kinds of prompts you might want to feed your into LLM.
Prompt wording means using the right words when writing your prompts for the AI model and using the correct technical terms. This basic prompt requires technical expertise and domain knowledge.
Succinctness – being as clear as possible in the wording of your prompt to enable the system to understand what you mean. Do not use more words than necessary to construct your prompt.
Roles and goals – assigning a clear role to the LLM as well as setting a goal to aim for so as to create effective outputs. This means knowing who exactly is meant to be writing the documentation and the target user.
Positive and negative prompting – incorporating commands to either do something or not do something to guide the LLM in its response. The AI understands that there is a right and a wrong answer.
Input/output prompting – clearly defining the input you want to feed into the LLM as well as the output you require, such as a script. You feed the correct data into the LLM in order to produce the correct output.
Zero-shot prompting – inputting quite a general instruction to the LLM so as to avoid limiting the response, which means the tool can arrive at a wide range of answers.
One-shot prompting – similar to the zero-shot prompt except the prompt engineer provides a little more context to the LLM to help refine results, adding extra information about how you would like the AI to formulate your answer.
Chain-of-thought prompting – guiding the LLM through the critical thinking process needed to solve a problem in order to generate more accurate responses, allowing the AI to learn from your prompts.
Why Technical Writers Need to Master Prompt Engineering
Prompt engineering is becoming an extremely in-demand skill for technical writers who want to work with emerging technologies to produce better documentation. Companies seek writers with experience working with generative AI who can use tools such as ChatGPT to handle complex information.
Also, Check out our article on Best practices in writing content for Generative AI-based chatbots
Since the field is expanding rapidly, more and more jobs are becoming available for technical writers who can master prompt engineering. Technical writing is becoming more about crafting the content and ensuring that it is highly accessible to users, which AI can support you with.
Being able to manipulate these AI technologies means technical writers can stay relevant in their field and have access to highly lucrative positions. While technical writers are already “technical” in some sense, they can expand their skills to include generative AI and prompt engineering.
Steps to Master Prompt Engineering
Follow these steps to become a master of prompt engineering as a technical writer.
Research and familiarization
Develop your ability to conduct research and find the right information and data to feed into AI as prompts. The responses of the AI tools are only as good as the information supplied to it so technical writers should be adept at identifying the right sources to craft into instructive prompts.
Adapting to Evolving Technologies
AI, like all technology, is a field that is constantly changing, and the latest technology of the day may become hopelessly obsolete when a new product is released. Being able to understand the principles behind generative AI and how prompts can be engineered is key to adapting to an evolving landscape that requires technical writers to possess the latest skills.
Supporting Cross-Functional Teams
Technical writers using prompt engineering don’t work in a vacuum, and you’ll need to work with other departments such as product, engineering, project management, marketing, and sales to produce the right documentation with AI. Understanding what these teams require from the technical writer and managing expectations is key to success.
Crafting Effective Prompts
Being able to understand the mechanics behind effective prompts, including incorporating the use of programming languages, will enable you to master prompt engineering. Asking AI the right questions and tailoring your input to enable the tool to generate the right answers means technical writers must be smarter than the machine. Crucially, technical writers must be experts in their field in order to successfully use LLMs to their full potential.
Testing and fine-tuning
Inputting a single prompt is not going to generate the final product. You must test your prompts and iterate them over many versions in order to arrive at responses that are truly suitable for your users in terms of technical documentation. The AI must be trained to adapt to the right answers, without which the tool may become nonsensical and “hallucinate” errors.
Providing Context and Use Cases
The content you will be creating with LLMs does not exist in a vacuum – that means any documentation you create must contain context and be targeted at specific use cases in order to be useful. Ultimately, the AI is not conscious of your intent behind the prompt, so you must be ultra-accurate when providing the LLM with instructions for your documentation.
Using Prompt Engineering in Technical Writing
Documentation of projects
The number one use case of prompt engineering in technical writing is documenting products for end users, whether that might be engineers, scientists or the general public. Even when you use prompts to create the documentation, it still requires a background in technical writing to ensure you arrive at content that is acceptable for the business. AI is one tool that technical writers can use to achieve this goal.
Using prompt engineering, you can take colleagues’ inputs to generate documentation representing multiple groups’ interests. As long as you have a clear idea of what you want to achieve with the documentation, which ultimately should be to help users, AI tools can theoretically respond to complex instructions that generate a highly polished final product.
Automation and efficiency
One of the biggest advantages of using AI for technical writing is the ability to automate previously mundane tasks. Prompt engineers take this automation to the next level by creating documentation that previously required intensive labor and creative ability to produce the finished product, achieved quickly.
3 Best Tools for Prompt Engineering
Here are the three best tools for prompt engineering that we know of.
Document360 is Knowledge base software that uses AI to recommend tags and SEO descriptions for your documentation. This means that as you write the natural articles for your knowledge base, Document360 provides the metadata and SEO to enable users to discover your pages more easily.
ChatGPT is one of the most popular tools used for prompt engineering which Microsoft is now integrating into all of their enterprise tools. ChatGPT caused a sensation when it was released in 2022 by generating highly natural-sounding human-like responses to a variety of prompts.
GitHub is an AI-powered development platform that enables you to turn natural language prompts into code suggestions. This makes writing, testing and publishing your code much easier as you can use a feature trained on billions of lines of code, called GitHub Copilot.
Technical writers who take the time to learn prompt engineering may not only stay current with the latest technologies but also make themselves more employable. Being able to save time and produce more comprehensive documentation means you can help more users with fewer resources.
Technical writers are already “technical” in this traditional sense, and already using supporting technologies in their workflows. Generative AI provides opportunities to use existing skills to create highly informative content which is then reviewed by experts – technical writers.
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