What is Generative AI anyway?

GenAI aims to produce new content, like text, images, or music, that’s indistinguishable from those created by humans. These models rely on machine learning algorithms to generate original and creative outputs—or content, in layman’s terms—based on input data.

Large language models (LLMs) are a subset of GenAI models, designed to understand and replicate human-like language. LLMs are fed vast amounts of text data so they can learn the patterns and structures of human language. The more training data they consume, the better they’re able to create accurate and relevant responses.

Of course, just like human learning, the quality of an AI models’ output depends on the quality of data it was trained on. Diverse, high-quality data improves: 

• Performance

• Quality

• Adaptability

• Ethical integrity

Developers who employ good data from the very beginning can harness the full potential of LLMs while mitigating risks associated with bias, privacy, and intellectual property.

The basis of AI training: prompt and response

AI Trainers use a set of key terminology to work on tasks and projects. We’ll cover some of the most common keywords to better explain what the role involves. 

What’s a prompt?

A prompt is the initial input given to an AI model with the goal of shaping their output toward a specific outcome. To put it simply, it’s the combination of text and instructions the trainer inputs into the model. 

Prompts can range from very simple, like write me a story about sunflowers, to extremely niche and complex domain-specific tasks. A few common examples of prompts include:

• Question answering

• Creative writing tasks

• Sentiment analysis

• Classification tasks

With well-crafted prompts, AI Trainers guide the language model's responses and ensure that the generated content aligns with expectations. These expectations are usually based on the project guidelines developed by the client. 

Sometimes, training AI is about telling it what not to do. In an effort to develop fair and ethical models, some projects might include prompts that are designed to be harmful or offensive. This method acts as a “test” of sorts, to determine whether the model will create a similarly offensive or harmful output. 

Every role, from AI Trainers to Domain Experts to Quality Assurance, works with prompts in some way. The prompt is one half of the foundation of AI Training, the other half being a response. 

What’s a response?

A response follows a prompt in the AI training sequence. The response is the model’s output based on its processing of the input, or prompt. Responses can range from concise single-sentence answers to comprehensive, multi-turn dialogues. 

AI Trainers help refine these responses with rigorous training and continuous optimization through meaningful feedback and informative interactions with the models. This can look like:

• Rating responses on a specific scale or rubric.

• Editing responses to be more relevant, accurate, or harmless. 

• Correcting the model through follow-up prompts.

Each project will have different objectives and sets of instructions when it comes to prompts, but they’re all designed to improve the quality and relevance of a response. This is crucial in building effective communication between users and AI models.

Turns vs. dialogues

In the context of training AI, turns refer to a set of prompts and their corresponding model-generated responses. Each turn consists of one prompt, the input, and its corresponding response, the model’s output based on the provided prompt. 

Multiple turns collectively form a dialogue, which is a sequence of alternating prompts and model-generated responses. Dialogues are essential for training LLMs as they help the model learn how to generate relevant responses based on different types of input and hold a coherent and organic conversation.

The role of AI Trainers

AI Trainers play an essential role in the process of prompt and response generation. From meticulously crafting individual prompts to orchestrating complete dialogues, AI Trainers employ their writing prowess on every task. Their efforts are the key to creating more precise, significant, and cohesive AI-generated output.

Human-powered: why do AI models need human input?

The World Wide Web went public in 1993. By late 1995, the internet accounted for only 1% of web traffic. Fast forward 20 years later, and the internet is our main source of news, entertainment, learning, and socializing, not to mention how indispensable it is to almost every single industry. 

All that to say, there’s a lot of content on the internet. Some of this data is excellent—diverse, high-quality, ethical, and informative—but there’s also a lot of junk out there. Human input via AI Trainers helps developers avoid issues that arise from bad data, including bias, ethical and moral dilemmas, and incomplete or incorrect outputs.

Avoiding bias

Some models are trained on existing texts or datasets and the quality and reliability of these inputs are not always guaranteed. This data might be biased, unethical, or contain inaccuracies, which can adversely affect the performance and output of the AI model. AI Trainers ensure that the training data provided to LLMs is accurate, diverse, and ethically sound.

Adding domain-specific knowledge

Teaching a model to perform tasks in a specific domain requires more than just generic texts from the internet. It needs the input of individuals who possess unique knowledge and expertise within that particular field. 

AI Trainers and Domain Experts bring domain-specific knowledge and insights to the table, helping them craft prompts that are relevant, contextually accurate, and aligned with the requirements of the project.

For example, in the medical domain, it's crucial to ensure that the data generated by AI Trainers isn’t harmful. This includes making sure that the responses do not provide direct recommendations, and instead, contain relevant warnings or precautions to guide users safely.

Ensuring diversity

Generating prompts in various styles, tones, and perspectives is crucial for effective training. Different users may interact with AI models in diverse ways. For example, a student might inquire about a term seeking explanations and information, while a professional in the workforce might ask about the same term searching for application. 

AI Trainers ensure that models are exposed to a wide range of prompts and responses, reflecting the diversity of real-world interactions. This helps improve the model's adaptability, responsiveness, and overall performance across different user scenarios.

Continuous learning

Every day, experts around the world are making new discoveries, breakthroughs, and inventions. Artists are creating new content, writing innovative stories, and disrupting the art world. All around the world, major events are happening all the time. 

AI training isn’t a one-and-done situation. Models need continuous learning to stay up-to-date, relevant, and accurate, and they require support from AI Trainers to do so. 

Be the mind behind the AI

At Mindrift, innovation meets opportunity. 

We're a pioneering platform dedicated to advancing the field of AI technology through collaborative projects with AI Trainers, Writers, Editors, and Domain Experts. Our focus on Generative AI data creation offers a unique chance for freelancers to contribute to AI development from anywhere, at any time.

If you've ever wanted to train AI models, join our community. Explore our different roles to see where you fit in and apply today!