Are you envisioning the next big thing in corporate learning? Or perhaps you’re stepping into the realm of AI-powered training solutions for the very first time? Either way, this indispensable guide is tailor-made just for you! In this article, I will unravel the intricacies of developing custom Generalized Training Protocols (GTPs), igniting a thrilling journey of learning engagement within your organisation—an exciting new dawn beckoning on the horizon.
“Developing custom GTPs is not just about embracing innovation—it’s about making more impact with your learning and development budget.”
We will explore how you can choose the best AI model for your specific goals. We’ll also detail best practices for building your application and guide you through the process of data sourcing. We will show you where the necessary is to accelerate and maximise your learning and development budget, assisting you to harness the potential of your existing learning assets and user data, enabling you to enhance your learning outcomes and get more out of your existing learning resources.
So, let’s begin our journey through the exciting world of custom GTPs.
| Steps for Developing Custom GTPs | Description | Tools/Methods |
|---|---|---|
| 1. Selecting the Right AI Model | Choose an AI Model based on specific outcomes needed. | ML-based tools like TensorFlow, PyTorch |
| 2. Designing the Application | Design an application to represent the selected model, keeping user ease in focus. | Wireframes, Prototyping tools |
| 3. Developing the Application | Develop the application using a suitable software language. | Python, PHP, JavaScript |
| 4. Data Sourcing | Collect necessary data from available learning assets and user data. | Data scraping, Data analysis tools |
Define the scope of the project and outline your statement of works
The first step in designing and developing an application for Generative Pre-training Transformers (GPTs) is clearly defining the AI outcomes you want to achieve; this means writing a clear statement of works which outlines the business objectives, the technical requirements and the user acceptance criteria that clearly articulates “done” and “good”. This involves understanding your organisation’s learning needs and how AI can be used to meet these needs. This could be anything from improving learning engagement, learning in the work flow, personalising learning experiences, or enhancing knowledge retention.
Select the suitable fundamental model and or more likely fundamental models (you may need more than one)
Once the AI outcomes have been defined, the next step is to select the right fundamental model. Various models, such as GPT-3, BERT, and Transformer-XL, are available, each with strengths and weaknesses. Choosing a model that aligns with your desired learning outcomes and the nature of your learning data is essential.
After selecting the model, the next phase is to design the application architecture. This involves deciding on the technology stack, the data flow, and the user interface. The technology stack should be chosen based on the model’s requirements, while the data flow should be designed to ensure efficient data processing and model training. On the other hand, the user interface should be intuitive and user-friendly, promoting easy interaction with the AI.
Designing the application with particular attention to the UI/UX
Designing the application involves creating a user-friendly interface that facilitates interaction between the learners and the AI. This includes intuitive navigation systems that are synergistic with your other applications, ensuring the application is accessible on various devices at a speed that works wherever you are in the world. Central to this is the user journey, ensuring the application provides a seamless and engaging learning experience mainly on the mobile phone, where learners engage most.
Begin coding up and integrating your data via APIs where possible.
The development phase follows the design phase. This involves coding the application based on the design specifications. Following best practices in software development is crucial to ensure the application is robust, secure, and scalable. This includes writing clean and efficient code, having quality control, automating testing in place, implementing error handling, and conducting thorough testing at every deployment.
Developing the application involves coding the design into functional software, including integrating the GPT model into the application, setting up databases for storing learning materials and user data, and implementing security measures to protect user data. It’s advisable to adopt agile development practices, allowing for iterative testing and refinement of the application.
Label, organise and clean your data and data sources
Parallel to the development, data sourcing is another critical step. This involves identifying and gathering your organisation’s learning assets and user provisioning data. The quality and quantity of data directly impact the effectiveness of the GPT’s; API’s can be used to connect databases that store learning materials and user data. Examples of learning data within your organisation could be as simple as employee skill assessment results or as complex as in-depth surveys about learning experiences.
Exactly what data you need hinges heavily on the specific AI outcomes you seek. For instance, if your learning department aims to personalise learning experiences, you might want to consider gathering data about previous job roles, professional achievements, individual learning styles, or areas where particular employees excel. This data cohort helps to refine your AI’s ability to recommend customised learning paths tailored to an individual’s unique learning style or career pathway. On the other hand, if the goal is to enhance learning outcomes generally, you might focus on broader data points such as course completion rates, examination results, or feedback on learning content. Analysis of this type of information can help shape more effective learning materials and delivery methods.
An often under-explored resource within an organisation is legacy data such as the SCORM, xAPI and document libraries within your Learning management system, archived over years of performance reviews and learner ratings and performance. This could be a treasure trove helping decipher learning patterns and adaptability to past pieces of training and could significantly expedite your GTP development process. Remember, your data is only as good as its collection method, so it’s always better to automate through the use of APIs so that new data matches new content you upload to your Learning platform.
Always ensure the data gathered is unbiased. This can be a challenge if you are delivering to a global audience where, for example, the definition of leadership may be subjective and aligned with cultural norms. While this task might seem daunting if you have a good learning management system (LMS) and or human resource information system (HRIS), there will be shortcuts to defining and routing data to your custom GTP model, as these systems hold much of the required data. Data from these systems and any 3rd party content platforms such as Linked In Learning or Open Sesame, will shortcut the development of the GTP and ensure the right data is available to feed your AI model. Once this is done right, you’ll find that your learning department has not just moved up a notch but leapfrogged into a new era of engaging learning experiences.
Testing, user acceptance testing, review and document lesson learned
Once the application is developed and the data is sourced, the next step is to train the model. This involves feeding the data into the model and adjusting the model’s parameters to minimise the difference between the model’s predictions and the actual outcomes. We adopt agile development practices that allow for iterative testing and refinement of the application, but its a phased approach so each phase of each POC can be reviewed, lessons learnt are communicated and a new Statement of work is created to define the parameters of POC 2. This process, known as machine learning, enables the model to ‘learn’ from the data. This should be a binary process in so much as the business requirements should dedicate what is defined as “done” and “good”, so that the POC can be launched and phase two of development can start in response to
Finally, it’s time to deploy the application after training the model. This involves integrating the application with your existing learning management system and ensuring it works seamlessly. After deployment, it’s essential to monitor the application’s performance and make necessary adjustments to improve the learning outcomes continuously – hello, POC 2.
Your organisation is a treasure trove of valuable learning data. By harnessing it, you’ll be able to create dynamic, engaging, and transformative learning experiences. Remember – the more high-quality data you feed your AI model, the better it will get in tailoring personalised learning paths and igniting a new dawn of learning engagement within your organisation. This may seem daunting, but with the right partner, strategy, tools, and approach, crafting a custom GTP can lead to groundbreaking changes in how your organisation learns. So, are you ready to transform your corporate learning department?