Understanding the terminology surrounding AI (Artificial Intelligence) is essential as this technology becomes a driving force in industries worldwide. Whether you're an AI enthusiast, a tech professional, or someone curious about the field, this list of important AI-related words will help you grasp key concepts quickly.
Here’s a breakdown of the most critical AI terms you need to know:
1. LLM – Large Language Model
Definition: A type of AI model designed to understand and generate human-like text.
Explanation: These models, like GPT or BERT, are trained on vast amounts of text data to predict and generate coherent responses based on input. They power applications like chatbots, content generation tools, and language translation services.
2. Workflow
Definition: A sequence of tasks or steps designed to achieve a specific outcome in AI projects.
Explanation: In AI, workflows organize processes like data collection, model training, testing, and deployment. They ensure efficient and systematic development.
3. RLHF – Reinforcement Learning from Human Feedback
Definition: A machine learning approach that improves AI behavior by incorporating human feedback.
Explanation: By combining reinforcement learning with guidance from humans, RLHF trains AI to produce responses that align with desired goals, making them more useful and safer for users.
4. Prompt
Definition: A text or data input given to an AI model to trigger a response.
Explanation: A well-structured prompt ensures that the AI produces accurate, relevant, and actionable results, whether answering a question or generating product content.
5. Prompt Engineering
Definition: The practice of crafting specific input instructions (prompts) to guide AI output effectively.
Explanation: Good prompts help AI tools like LLMs generate accurate and relevant results. It’s an essential skill for maximizing the utility of generative AI systems.
6. Multi-Modal Prompts
Definition: Prompts that combine different types of data, such as text, images, and audio, to interact with AI systems.
Explanation: Multi-modal AI systems, like those powering image or video generation, require multi-modal prompts to process and generate output across multiple formats.
7. AI Agent
Definition: An autonomous entity powered by AI that can perform tasks or make decisions without human intervention.
Explanation: AI agents can range from chatbots to complex systems managing business processes or performing scientific analyses. They operate based on pre-set goals and learned behaviors.
8. Generative AI
Definition: A branch of AI focused on creating new content, such as text, images, music, or code.
Explanation: Tools like ChatGPT or DALL·E use generative AI to produce human-like outputs, often transforming industries like marketing, design, and entertainment.
9. Fine-Tuning
Definition: The process of customizing a pre-trained AI model for a specific task or dataset.
Explanation: Fine-tuning refines the AI's performance, enabling it to produce better results for niche applications, such as medical diagnoses or legal document analysis.
10. Machine Learning
Definition: A subset of AI where machines learn patterns from data without explicit programming.
Explanation: It forms the backbone of most AI systems, enabling them to improve and adapt over time.
READ MORE: What is Product Data Enrichment and Why Is It Important?
11. Natural Language Processing (NLP)
Definition: A field of AI focused on enabling computers to understand, interpret, and generate human language.
Explanation: NLP powers applications like language translation, sentiment analysis, and voice assistants.
12. Model Training
Definition: The process of teaching an AI model to recognize patterns using data.
Explanation: Training involves feeding data into the model and adjusting it until it can perform tasks like predictions or classifications accurately.
13. Pre-Trained Model
Definition: An AI model that has already been trained on a large dataset and can be adapted to specific tasks.
Explanation: Using pre-trained models saves time and resources by building on existing capabilities.
14. Deep Learning
Definition: A type of machine learning using artificial neural networks with many layers.
Explanation: Deep learning powers complex tasks like image recognition, natural language understanding, and self-driving cars.
15. Token
Definition: A unit of data, often a word or character, that an AI processes when generating or analyzing text.
Explanation: Understanding tokens is crucial for managing input length and optimizing AI responses.
16. Data Augmentation
Definition: Techniques used to increase the diversity of a dataset by creating modified versions of existing data.
Explanation: It improves model training by simulating variations in real-world data.
17. Embedding
Definition: A numerical representation of data (like words or images) that an AI model can process.
Explanation: Embeddings are essential for enabling AI to understand relationships between concepts.
18. API – Application Programming Interface
Definition: A set of rules and tools that allow applications to communicate with each other.
Explanation: Many AI services, like LLMs or our AI engine, provide APIs to integrate their capabilities into other applications.
19. Bias in AI
Definition: A tendency of AI models to produce skewed or unfair results due to biased training data.
Explanation: Addressing bias is crucial for creating fair and ethical AI systems.
20. Zero-Shot Learning
Definition: A method where AI performs tasks without prior training on specific examples.
Explanation: Zero-shot learning allows AI to generalize and adapt to new scenarios efficiently.
Did we miss a term? Let us know, and we’ll expand the glossary.
