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Whether you are talking with customers, or employees, a great conversation is key to a great relationship. Conversational AI lets you scale conversations at an unprecedented level.
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AI Training is the art of teaching AI assistants to understand human language. The better you understand what your audience is talking about, the more relevant answers your chatbot or voice assistant can provide.
Technology can already process human language at a basic level, but not without the help of people that understand how to get the most out of this technology: ‘AI Trainers’.
AI trainers teach AI assistants to understand human language. They do this by feeding examples of real utterances to help the AI assistant better understand the meaning of peoples speech, or written language.
AI trainers analyze common topics discussed by users and how they ask for certain information while talking to your chatbot or voice assistant. These insights are used to continuously improve the cognition of your AI assistant and require a structured AI training workflow consisting of testing, updating, and measuring again.
As Conversational AI technology evolves, the role of the AI trainers evolves as well. Part of the work of an AI trainer is to refine their techniques and adapt to the technology landscape, especially with the advent of large language models (LLMs).
AI trainers gather and prepare data required to train an AI assistant. This involves tasks like sourcing and preparing data and optimizing the (language) model. They are usually also involved in the implementation of dialogues in the conversational AI platform.
Whether you’re building a declarative chatbot or generative AI chatbot, you have to know how to work with the model available. Declarative chatbots work with natural language understanding (NLU) algorithms, whereas generative AI chatbots leverage large language models (LLMs). Hybrid chatbots leverage a combination of both.
AI trainers are often tasked with sourcing and preparing training data to feed into the models. Depending on the chatbot you’re building and the domain-specific focus, the training data is tailored to specific topics and contexts. Cleaning training data is essential if you’re looking to improve the accuracy of the model.
Most companies build and maintain their chatbot on a Conversational AI platform. Every platform has its own capabilities and limitations and part of the responsibility of the AI trainers is to understand and navigate that.
After training and implementation, the AI trainer tests the performance of the AI assistant on a regular basis, using various testing methods such as k-fold cross validation, blind testing and more.
After deployment, the AI trainer continues to monitor model performance, and improve it on a continuous basis especially as the corpus of the chatbot or voice assistant grows.
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For a declarative chatbot, the Conversational AI dataset consists of a collection of examples or ‘utterances’ of how people ask for what they want. Ideally, this data is sourced from real conversations with customers, such as phone transcripts or live chat conversations. This data is then cleaned and used to train the AI assistant.
Here are some key characteristics of conversational AI datasets:
Most Conversational AI datasets consist of ‘natural’ language, typically written text. Often a dataset contains individual sentences, or words, grouped around similar meaning or context.
Most types of human language are varied and context-specific. The better your dataset addresses these nuances, the better the understanding of your AI assistant becomes.
For NLU based chatbots, it is important to spread the amount of training data evenly across your intents to avoid over- or undertraining.
Some Conversational AI datasets include multimodal data, which usually consists of text combined with images, videos, or audio recordings. This is more common for large datasets, like the ones used to train multimodal models like GPT-4o and Gemini.
When curating Conversational AI datasets, it's essential to consider ethical considerations such as privacy, bias, and fairness. Ensuring that the dataset is diverse, inclusive, and representative of all of the humans that might interact with the intended AI assistant helps mitigate the risk of bias or discrimination.
AI training empowers businesses to leverage their Conversational AI more effectively. The better your AI assistants are able to grasp what your customers are talking about, the better the service they can deliver, and the more conversation you can automate. Core benefits of AI Training:
Deciding when to hire or upskill an employee to become an AI trainer for your conversational AI project depends on various factors, including the current stage of your AI project, your organization's needs and resources, and the complexity of your conversational AI solution.
Here are some considerations:
Make sure you have at least one AI trainer in your project from the start. Laying the groundwork is crucial for long term success.
Look within your team for relevant profiles. Consider external hiring or training if your organization doesn’t have the skillset in-house.
Advanced techniques require a specialized AI trainer, for example when you design to migrate your NLU chatbot to an LLM-powered solution.
If you have ambitious timelines or goals, you might want to consider relying on the consulting services of CDI.
Make sure you have a hiring strategy in place that accounts for the growing complexity, maintenance, and monitoring of your conversational AI solution.
Ultimately, the right time to hire or train an AI trainer depends on a combination of these factors. It's essential to assess your current needs, capabilities, and strategic objectives to make an informed decision about when to invest in AI training resources.
Understanding of Conversational AI: Knowledge of the principles and challenges involved in building conversational AI solutions, including dialogue management, intent recognition, entity extraction etc.
Industry expertise: Familiarity with the specific industry or domain in which the conversational AI solution will be deployed can be valuable for understanding user needs, language nuances, and domain-specific requirements.
Educational background: A degree in computer science, data science, artificial intelligence, or a related field can provide a solid foundation for the role.
Certifications: Making sure your AI trainer is CDI-certified is solid proof that they understand the foundations of AI training and are committed to learning.
Analytical thinking: Ability to analyze complex problems, break them down into manageable components, and develop effective solutions.
Curiosity: Eagerness to learn new concepts, stay updated on emerging trends in AI, and adapt to evolving technologies and methodologies.
Communication skills: Strong verbal and written communication skills are essential for explaining technical concepts, collaborating with cross-functional teams, and presenting findings to stakeholders.
Problem-solving: Capacity to approach challenges systematically, experiment with different approaches, and troubleshoot issues effectively.
Attention to detail: Meticulousness in data analysis, model evaluation, and documentation to ensure accuracy and reliability in AI training processes.
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