Understanding User Intent

1. Understanding User Intent (The Input)

Large Language Models (LLMs), fundamentally built on the Transformer architecture, process your text by converting it into a mathematical representation that captures the full context of your query.

Tokenization and Embedding

The process begins by breaking down your input into smaller units called tokens (words, sub-words, or punctuation). Each token is converted into a numerical embedding — a vector of numbers positioned in a high-dimensional space where tokens with similar meanings (like “king” and “queen”) are numerically close. This provides the foundation for semantic understanding.

The Attention Mechanism

The Self-Attention mechanism lets the model determine how much weight each word in your sentence should have relative to others. For instance:

Example: In the phrase “The bank felt soft,” the model links “bank” to “soft,” interpreting the word as a riverbank rather than a financial institution.

By computing these attention weights, the model forms a contextualized understanding of your request — recognizing your intent (e.g., to learn, to write, or to generate code).

2. Generating a Response (The Output)

Response generation is an iterative process of predicting the next most probable token until the complete answer forms.

Next-Token Prediction

The model calculates a probability distribution for each token in its vocabulary based on your prompt and conversation history, then selects the most appropriate next token, appends it, and repeats — until an “end of sequence” signal appears.

Decoding Strategies

Modern models introduce creativity and diversity using these techniques:

• Temperature: Controls randomness. Low values (0.1) yield factual, stable results; high values (0.9) encourage creative expression.
• Top-K / Top-P Sampling: Restricts token choices to the top-K most probable, or the smallest set whose total probability exceeds threshold P (Nucleus Sampling).

Retrieval-Augmented Generation (RAG)

For fact-based or time-sensitive questions, models use RAG or tool-use methods to incorporate real-time information:

1. Detect when external data is needed (e.g., “What is the news today?”).
2. Pause generation to query a search engine or knowledge base.
3. Feed retrieved snippets back into the model as added context.
4. Generate a final answer grounded in accurate, current information.

This drastically reduces factual errors, or hallucinations.

3. Refinement and Alignment

The last stage ensures that responses are not only accurate but also safe, ethical, and helpful. This process is called Alignment.

Reinforcement Learning from Human Feedback (RLHF)

RLHF fine-tunes models to align with human preferences through three phases:

1. Human Feedback: Annotators rate AI responses for helpfulness, clarity, and accuracy.
2. Reward Model: These ratings train a secondary model that learns what a “good” answer looks like.
3. Reinforcement Learning: The LLM is optimized to maximize scores from that Reward Model, improving real-world performance.

Thanks to alignment, when you ask, “How do I bake a cake?”, the model delivers a clear, step-by-step recipe rather than a random paragraph about baking history.