In this second installment of our AI/ML terminology series, we break down essential concepts that power modern large language models (LLMs) and machine learning systems. From how data is tokenized to how models generate coherent responses, these terms are foundational for anyone working with AI.
Tokens and Tokenization Tokens are the smallest units of text that an LLM processes, which can be words, subwords, or characters. Tokenization is the process of converting raw text into these tokens. For example, the word "unhappiness" might be split into "un", "happi", and "ness". This allows models to handle a vast vocabulary efficiently.
Embeddings Embeddings are numerical representations of tokens in a high-dimensional vector space. Each token is mapped to a vector, and similar tokens are placed close together. This enables the model to understand semantic relationships, like "king" being closer to "queen" than to "car".
Transformers and Self-Attention The transformer architecture revolutionized AI by using self-attention mechanisms. Self-attention allows the model to weigh the importance of different tokens relative to each other, capturing context even across long distances. This is why transformers excel at tasks like translation, summarization, and question answering.
Hyperparameters: Temperature, Top-P, and Top-K These control the creativity and diversity of model outputs. Temperature scales the probability distribution: lower values make outputs more deterministic, higher values increase randomness. Top-P (nucleus sampling) selects a set of tokens whose cumulative probability exceeds a threshold. Top-K limits the sampling to the K most likely tokens. Tuning these helps balance coherence and novelty.
Guardrails and Bias AI guardrails are safety measures to prevent harmful or biased outputs. They can be rule-based filters or model-level constraints. Ensuring fairness and reducing bias is critical for deploying AI responsibly. Techniques include dataset balancing, adversarial debiasing, and continuous monitoring.
Inference Types Inference is the process of using a trained model to make predictions. It comes in several forms: batch inference (processing many inputs at once), real-time inference (low-latency for interactive apps), and edge inference (running on devices like phones).
This tutorial is ideal for beginners, developers, and engineers looking to build strong skills in AI, ML, and MLOps. Understanding these terms helps you build scalable, real-world AI systems and improve your ability to work with LLMs, design better prompts, tune outputs, and deploy safe applications.