Artificial Intelligence is already a part of daily operations across many industries. It’s driving process acceleration, boosting efficiency, and elevating project delivery to a new level. Even so, many professionals still struggle to understand or use common AI terms. Learning the vocabulary of AI makes it easier to interact with partners, make better decisions, and even pursue real innovation for your company.
At NextAge, we use AI as a driver of efficiency and results at every stage of development. With that in mind, we put together this dictionary so you never get lost in AI jargon again.
AI Dictionary
Machine Learning
Machine Learning is when a system is programmed to learn from data without being explicitly coded for each task. It recognizes patterns, adapts, and improves performance over time. For example, think of a recommendation system in e-commerce that suggests products based on previous purchases, that’s machine learning working behind the scenes. According to the “Machine Learning Market Size, Share & Trends Analysis Report,” the global ML solutions market is set to grow by an average of 38% per year until 2030.
LLMs (Large Language Models)
LLMs are language models trained on massive amounts of data, from books to articles and conversations. They generate new content, answer questions, and translate text with a high degree of contextual understanding. ChatGPT is a popular example. This kind of technology can transform everything from customer support to the large-scale creation of technical documentation.
Deep Learning
Deep Learning is one of the cornerstones of modern AI. It leverages deep neural networks, structures inspired by the human brain, to tackle complex tasks such as facial recognition, image analysis, or even medical diagnostics. This technology is behind recent breakthroughs in computer vision and unstructured data analysis.
Training and Inference
AI works in two phases. During training, the AI learns from massive datasets, adjusting its internal parameters to minimize errors. Inference happens when the system uses what it has learned to make predictions (“Does this photo show a cat or a dog?”) or provide real-time answers. Each phase requires different resources and considerations.
Generative AI
Generative AI goes beyond analysis and actually creates new things. It produces text, images, music, and original code, all based on what it has previously learned. Companies use this technology to speed up marketing campaigns, generate prototypes, or test ideas with much greater agility. A McKinsey study estimates that generative AI could add up to $4.4 trillion per year to the global economy (McKinsey, “The economic potential of generative AI”).
Prompt
A prompt is the initial command or instruction you give to AI to get the result you want. The clarity of your prompt can make all the difference. In corporate environments, knowing how to ask the right way can save time and ensure more accurate automation.
Dataset
A dataset is simply the collection of data used to train and evaluate AI. It can include text, images, spreadsheets, or any other relevant information. Having a good dataset is crucial for effective and reliable AI, if your data is low quality, the output will be too.
Hallucination
Hallucination occurs when an AI generates incorrect or unfounded information, even while appearing confident. This remains a real challenge for practical applications, so human validation and double-checking are essential parts of responsible AI use.
Fine-tuning
Fine-tuning means customizing pre-trained models for specific use cases, using a company’s own data. This way, the AI understands industry jargon, business patterns, or custom rules, and delivers results that are more tailored to your particular reality.
Intelligent Automation
It’s not just about automating tasks, it’s about using AI to spot opportunities, prioritize demands, and free up teams for more strategic activities. Intelligent automation lets companies accomplish more and do it better, without needing to expand the workforce.
How NextAge uses AI in development
As innovation evolves, AI is becoming more and more integral at every stage of our projects. In the development phase, we automate repetitive tasks, speed up testing, and deliver quality with a forward-thinking mindset.
Our Staff Augmentation model, with multidisciplinary squads and close involvement from Tech Leads, can cut project costs by up to 40%, while providing flexibility, scalability, and transparency. Everything is backed by straightforward contracts and robust compliance.
Want to learn how AI can accelerate your business growth? Get in touch with NextAge and discover tailored solutions for your needs.
