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What is an AI Trainer?

What is an AI Trainer and what do they do? AI Trainers work on building chatbots and training their AI models.
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You've heard of chatbots, but have you wondered about who trains these bots? The short answer: AI trainers.

AI trainers specialize in training chatbots to respond accurately and efficiently to customer inquiries. They're also responsible for the development and maintenance of such chatbots. 

They create, train, and deploy automated conversational experiences in various languages and contexts. They intend to ensure that the bots function in line with industry trends and customer demands. As such, these professionals must be knowledgeable about AI technology and customer service best practices to create effective chatbot solutions. 

Let's look at what AI trainers do, their responsibilities, the skills required, and what kind of businesses hire them.

What are the tasks and responsibilities of an AI trainer?

AI trainers have a broad range of responsibilities that span multiple disciplines, including coding, machine learning, linguistics, and psychology.

Here's a list of a varying set of responsibilities that AI trainers work daily:

Setting up the virtual assistants or chatbots

This process involves developing the conversation flow and rules using popular bot tools like ManyChat, Botsify, etc., to achieve that. It includes deciding the conversation topics and designing natural, intuitive, and engaging dialogues. 

AI trainers must also establish the conditions under which conversations will take place and determine how different user inputs should be interpreted and responded to. Additionally, they create intents and entities to capture user information and processes to automate frequent tasks using natural language processing (NLP) techniques.

Setting up the NLP/NLU model

AI trainers train the AI model by creating training phrases to teach the bot how to interpret user inputs. They must label the training phrases and associate them with their corresponding intents. For example, if you choose a phrase like "change my address," the bot will make the necessary changes.

Additionally, they must identify entities, sets of related words, or groups of words that may have a semantic relation. Examples of entities include synonyms, hyponyms, and other terms associated with specific intent. Once this data is collected, AI trainers can build an NLP/NLU model.

Implementing the conversational flows

AI trainers must be knowledgeable about language and computer science and understand how users interact with technology. They create intents and also test and debug the bot to ensure it functions properly. They have to provide all interactions between the user, and the bot is natural-sounding, so they don't cause confusion or frustration. 

Additionally, they implement slot filling, a data collection technique that allows users to move forward in conversations only after providing the required information. They also set up operations that will enable bots to perform actions like sending emails or unsubscribing from newsletters when needed. 

Conducting quality assurance checks

AI trainers are responsible for validating the conversational flow and checking if the bot functions as intended. They must carefully examine each response to ensure accuracy, speed, and fluency. 

They use testing techniques such as A/B testing or regression testing to validate the effectiveness of their new model before implementation. 

Additionally, they work with a quality assurance (QA) checker to ensure there are no errors or inconsistencies in the design.

Analyzing and improving the chatbot

To ensure the bot provides accurate answers to users' inquiries, it requires them to perform data analysis and mining on collected conversations with users. Using this, they can identify issues such as misclassification errors or lack of relevant information. Plus, they can identify opportunities for improving the user experience by spotting trends in user behavior or preferences.

For example, they can employ a tool like Dashbot’s Report module to analyze critical chatbot metrics and iterate till it functions optimally.

What tools do AI trainers use?

Here’s a list of some of the must-have tools in every AI trainer’s toolkit:

NLP engines

  • Google Dialogflow: Google Dialogflow is an artificial intelligence (AI) platform developed by Google Cloud. It enables trainers to build conversational user interfaces and integrate them into existing applications, bots, websites, and devices. Using NLU, the platform makes it easier for trainers to develop conversational AI experiences that can understand human conversations and respond with relevant data. 
  • Amazon Lex: Amazon Lex is a powerful AI tool that allows trainers to create natural conversations and interactions with lifelike accuracy. AI trainers can use Amazon Lex to create voice-driven applications using NLU and speech recognition that respond accurately and quickly to user inquiries. 
  • IBM Watson: Watson is a question-answering computer system developed by IBM. It uses natural language processing and advanced algorithms to answer questions posed by users. It also offers suggestions on how to improve the accuracy of the response given.

Large language models

  • GPT-3: Generative Pre-trained Transformer 3 (GPT-3) is a powerful autoregressive language model developed by OpenAI. It uses deep learning to generate human-like text in response to prompts given by the user. GPT-3 produces coherent, well-structured, and grammatically correct sentences that are on par with the quality of a human-generated text.
  • Cohere.ai: With Cohere.ai, AI trainers can quickly and easily generate or analyze large amounts of text for copywriting, moderation, classification, and information extraction. Cohere.ai also offers a wide range of features for trainers, such as real-time insights into content quality, built-in data validation tools, and personalized models based on the trainer's desired outcomes.
  • AI21 Studio: AI21 Studio is a comprehensive, cutting-edge platform developed by AI experts that allows trainers and developers to create and improve intelligent chatbots. The NLP model will enable users to leverage advanced context understanding, natural language processing, and machine learning technologies to make smarter and more intuitive bots.

Reporting tools

  • PowerBI: With Power BI, trainers can quickly turn raw data into meaningful insights that help them better understand the performance of their AI models. This powerful software platform offers various features, such as customizable dashboards, comprehensive data visualizations, automated report generation, and more.
  • Dashbot Report: AI trainers can benefit from the Dashbot Report platform to gain insights into customer behavior and conversations with their users. With the power of connected data processing, trainers can access real-time views of conversation histories and trends in an easy-to-use dashboard. By mapping out intent patterns, they can better understand how customers interact with their AI models, allowing them to adjust their strategies and improve performance. 
  • Tableau: Tableau Software is another option for AI trainers looking to create powerful visualizations and informative reports. Its easy-to-use drag-and-drop interface allows users to quickly generate various graph types to provide insight into complex datasets. Tableau's in-memory data engine enables users to explore, extract, store, and retrieve data.
  • Spreadsheets: You can also use an Excel or Google Sheets spreadsheet to track your metrics. However, you will have to build a report from scratch.

Where do AI trainers work?

Increasingly, businesses are utilizing AI trainers to help integrate AI into their operations to maximize efficiency and enhance customer services. It's a broad term that describes roles requiring a combination of knowledge of AI and a company's particular industry, a valuable position in various organizations. 

There are several places where AI trainers typically work. Here are a few examples:

  • Financial institutions setting up chatbots for customer support
  • Healthcare providers utilizing machine learning for research and drug development
  • Telecommunication companies automate maintenance and customer service tasks 
  • Internet companies are creating customizable intelligent assistants

What skills does an AI trainer need?

The range of skills can be further categorized into technical and soft skills. Here's what's expected under each of them:

Technical skills

  • Analytical skills: They should have comprehensive data analysis skills to work with large volumes of data effectively. It also requires them to understand how to store it securely and efficiently, explore it for valuable signals, and use various tools to analyze it. By doing so, they can troubleshoot malfunctioning bots and similar issues.
  • Domain expertise: They must also have a working knowledge of chatbot metrics such as Cognition, Confusion, and Containment and business-related metrics like customer satisfaction (CSAT) and net promoter score (NPS). This helps them keep the business's goals and customer intent in mind before building the bot.
  • Programming skills: They must have a working knowledge of AI and machine learning (ML). It helps them train the models to work based on the chatbot's end goal.

Soft skills

  • Communication skills: They must possess strong communication abilities since they often need to work with other stakeholders, including designers, developers, product managers, marketing teams, etc. They should be able to clearly explain complex concepts verbally and in writing to collaborate efficiently with their colleagues.
  • Problem-solving capabilities: They should have a wide range of problem-solving skills, such as creativity, strategic thinking, decision-making, and analytical abilities. AI trainers must think analytically to identify solutions to potential challenges when the bot doesn't function well.
  • Teamwork capabilities: These skills include the ability to motivate and lead, strong communication skills, active listening, and an understanding of the roles within a team. They must articulate ideas clearly and concisely without alienating any members or creating confusion in the group.

What kind of salary do AI trainers make?

According to Glassdoor, the average base pay for an AI trainer in the United States is $53,535 annually. In this case, the sample size is relatively small. However, similar sites like ZipRecruiter found that the national average is $95,309 per year

While it ranges from $20,500 to $196,5000, it depends on several factors, such as location, years of experience, level of education, and the scope of work.

The image shows data from ZipRecruiter's salary survey for AI trainers. It indicates that the US national average salary is $95, 309.
The image shows data from ZipRecruiter's salary survey for AI trainers. It indicates that the salary ranges between $45,000 and $174,000 depending on various factors.
Source: ZipRecruiter

Final word

In the future, we will use more conversational interfaces, businesses will use more chatbots and voice assistants, and the demand for AI trainers will grow. 

AI assistants powered by large language models like GPT-3 are more valuable and accurate when "trained." By default, language learning models are comprehensive and built to answer many generic questions. You can unlock their value by training them for specific use cases, i.e., marketing, customer support, and coding. It indicates that this is a viable field with a lot of potential to be uncovered in the near future.

If you’re looking to kickstart your learning journey, here’s a course recommendation we have for you: AI Trainer Certification Bundle.

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