How to train ARTIFICIAL INTELLIGENCE
Imagine you have a team that never rests, learns with every task, and improves over time. That's what you can achieve by training AI correctly. But how do you do it? And what real impact can it have on your business?
Si crees que entrenar una inteligencia artificial es solo para gigantes tecnológicos, te sorprenderás al ver cómo cualquier negocio puede aprovechar su poder. Sigue leyendo para descubrirlo.
What does training Artificial Intelligence involve?
Training artificial intelligence involves teaching it to recognise patterns in data so that it can make decisions or perform tasks autonomously. It is like training a new employee: at first, they need a lot of instruction, but over time their performance improves until they become an expert.
In technical terms, training involves feeding an AI model with large volumes of data, adjusting its algorithms, and allowing it to learn from its mistakes in order to optimise its accuracy.
This process is key to ensuring that Artificial Intelligence is truly useful and can add value to the company. Poorly trained AI can generate inaccurate or even harmful results, while well-trained AI can improve efficiency, reduce costs, offer personalised experiences to customers, etc.
How to train an AI
Training an AI does not happen overnight. It is a process that requires planning, quality data, and continuous evaluation. Here we explain the key steps to do it correctly.
>> Define the objective
Before you begin, you need to be very clear about why you want to use artificial intelligence. It is not about applying AI because it is fashionable, but rather to solve a specific task in your daily life.
For example:
A restaurant that wants AI to automatically respond to frequently asked questions on WhatsApp.
An online shop that wishes to recommend products based on its customers' previous purchases.
An advisor who receives many emails and wants AI to classify them as ‘urgent’ and ‘non-urgent’.
When you are clear about your objective, it will be easier for you to decide what data you need and what tool to use.
>> Collect and prepare the data
AI learns by observing examples. If you want it to recognise urgent emails, you will need to gather old messages and label them according to their priority. If you want it to recommend products, you will need your sales and customer data.
You can use tools you already know to do this:
Google Sheets or Excel to organise the data.
Notion or Airtable if you prefer a more visual interface.
ChatGPT, which can help you analyse and clean your data if you upload a file or use it with a custom GPT.
Remember: data quality is more important than quantity. It is better to have 100 well-organised examples than a thousand poorly classified ones.
>> Choosing the right AI model
Once the objectives have been defined and the data prepared, the next step is to choose the tool for training the artificial intelligence. There is no need to know how to programme, as there are visual solutions that guide you through the entire process.
Below are some of the most accessible ones, with real-life examples of their application:
ChatGPT (OpenAI)
Ideal for working with text: emails, enquiries or product descriptions.
Example: an advisor can upload a file containing emails labelled as ‘urgent’ or ‘non-urgent’ and ask ChatGPT to learn from these examples. The system will then be able to automatically classify new messages.
Microsoft Copilot Studio
Designed to create virtual assistants or chatbots that answer frequently asked questions.
Example: a dental clinic can use Copilot Studio to create an assistant that answers questions about prices, opening hours or treatments, freeing staff from repetitive calls.
Teachable Machine (Google)
A highly visual tool, ideal for training AI with images, sounds, or gestures.
Example: a clothing store can use Teachable Machine to teach AI to recognise types of garments and automatically classify photos of its products.
Lobe.ai
No-code alternative for training visual or classification models.
Example: A car repair shop can use Lobe.ai to have AI identify, based on photographs, whether a part is in good condition or needs repair.
Google AutoML
Google's solution for training predictive models without technical knowledge.
Example: A grocery store can use AutoML to analyse its sales data and predict which products will be in highest demand next month.
Runway ML
Focused on creating multimedia content and image or video models.
Example: a marketing agency can use Runway ML to generate personalised videos for each client, using their own material as a basis.
If this is your first time training an AI, it is best to start with ChatGPT or Teachable Machine, which are the simplest options and allow you to obtain quick results.
>> Train the model
The training process consists of teaching the tool with the prepared data.
Upload the examples (texts, images, or records).
Allow the tool to analyse and search for patterns.
Review the results and correct any errors.
Repeat the process until the system reaches the desired level of accuracy.
Here are some examples of how to train an AI:
- An agency can train ChatGPT with examples of real emails from its clients and the responses that staff typically send. In this way, the AI learns the tone, expressions, and type of information to include, and then drafts responses that only need a quick review before being sent.
- An online shop can use Teachable Machine to teach AI to recognise products from photographs. Simply upload several images from each category (e.g. “T-shirts”, “shoes”, “accessories”) and the tool will learn to classify them automatically, making catalogue management easier.
- A maintenance company can use Lobe.ai to train a model that distinguishes between images of facilities that are ‘in good condition’ and those that are ‘malfunctioning’. After showing enough examples, the AI can help the team identify incidents in photos sent by technicians, speeding up reviews.
>> Evaluate and adjust
Once trained, the AI must be tested with new data that it has not seen before. This serves to check whether it has learned correctly. If the results are good, the model is ready to use; if not, the examples must be adjusted or more variety added. The improvement process is continuous: each correction helps the AI learn better.
CONCLUSION
If you train artificial intelligence without the right data or a clear strategy, you will obtain inefficient results that could even be detrimental to your business. But if you do it right, AI can become your best ally for growing and optimising every area of your company.
Now that you understand the process and the benefits, what opportunities do you think AI can bring to your business?
