((Also read: Reasoning in AI))
1. What is Tokenization?
Tokenization is the process of breaking raw input text into smaller units called tokens, which can be words, subwords, or characters. These tokens are then mapped to numerical representations that models can process. In large language models (LLMs), subword tokenization (e.g., Byte-Pair Encoding) is commonly used to balance vocabulary size and representation flexibility.
2. What is Model Inference?
Model inference refers to the phase where a trained machine learning model generates outputs from new, unseen input data. In LLMs, inference typically involves processing tokenized input through the model to predict the next tokens or generate text, using the learned parameters from training.
3. What is Prompt Engineering?
Prompt engineering is the practice of designing and refining input prompts to guide a language model’s outputs toward desired behaviors or results. It involves crafting instructions, examples, or context in the prompt to improve accuracy, relevance, and controllability of model responses.
4. What are parameters in LLMs?
Parameters are the internal weights and biases of a neural network learned during training. In LLMs, they encode the statistical patterns and relationships across language; for example, GPT-3 has 175 billion parameters that collectively determine how it generates text.
5. What is AI Engineering?
AI engineering is the discipline of designing, developing, deploying, and maintaining AI systems in production environments. It integrates software engineering principles, data pipelines, model training, and operations to deliver scalable and reliable AI-powered solutions.
6. What is an Agent?
An agent is an autonomous system that perceives its environment, reasons or plans, and takes actions to achieve specific goals. In AI, agents can include conversational bots, decision-making systems, or self-learning entities capable of interacting dynamically with users or other systems.
7. What are Foundation Models?
Foundation models are large, pre-trained models—often trained on massive, diverse datasets—that serve as general-purpose building blocks for downstream tasks. Examples include GPT, BERT, and CLIP, which can be fine-tuned or adapted for specialized applications.
8. What is Generative AI?
Generative AI encompasses models and techniques that can produce new content, such as text, images, audio, or code, based on learned patterns. These systems create outputs that resemble human-generated data, exemplified by models like ChatGPT, DALL·E, and Stable Diffusion.
9. What is a Transformer Model?
A transformer model is a neural network architecture introduced by Vaswani et al. in 2017, which uses self-attention mechanisms to capture dependencies across input sequences. Transformers are the backbone of most modern language and vision models due to their scalability and effectiveness.
10. What are multimodals?
Multimodals refer to AI models capable of processing and integrating multiple data types (modalities) such as text, images, audio, and video. Multimodal models can understand and generate content that combines these different forms of information, enabling richer and more contextual AI capabilities.