What is LLM ?

What is LLM: A Beginner’s Guide

LLMs are sophisticated functions that predict the next word (token) in a sequence by assigning probabilities to all possible words.

Key Points:

• Training: Models learn from enormous amounts of text (GPT-3’s training data would take 2,600+ years to read). They start with random parameters that get refined through billions of examples using backpropagation.

• Scale: Training requires over 100 million years worth of computation if done sequentially, made possible only by GPUs running operations in parallel.

• Transformers (introduced by Google in 2017): Unlike older models that processed text sequentially, transformers process all text simultaneously using:

  • Attention mechanisms: Allow words to influence each other’s meanings based on context
  • Feed-forward networks: Store language patterns learned during training
  • Fine-tuning: After pre-training, models undergo “reinforcement learning with human feedback” where workers flag problematic outputs to align the model with user preferences.

How LLM Chatbot Works?

LLM chatbots take a user prompt, encode it into tokens, and generate a reply by repeatedly predicting the next token until a stopping condition is reached.

Conversation format

• Prompts are often structured with explicit markers to provide role and intent, e.g.:
  • User: or Q: for input
  • Assistant: or A: for the expected response
• Consistent formatting helps the model understand turn-taking and desired output style.

Generation process (high level)

• Tokenization: input text → discrete tokens the model understands.
• Encoding: tokens fed into the transformer to compute contextual representations (attention + feed-forward).
• Decoding: the model predicts the next token autoregressively and appends it, repeating until completion.
• Decoding strategies: greedy, beam search, top-k/top-p sampling, and temperature control how deterministic or diverse outputs are.

Context and transformers

• Attention lets tokens influence each other so responses reflect context from the entire input window.
• During training transformers process tokens in parallel; during autoregressive generation tokens are produced sequentially to preserve coherence.

Augmentation and retrieval

• Retriever-Augmented Generation (RAG) and similar patterns combine an information retrieval step with the LLM:
  • Retrieve relevant documents or facts.
  • Condition the model on retrieved context to produce more accurate, grounded answers.
• Useful for up-to-date or domain-specific queries the base model didn’t memorize.

Determinism and variability

• The model’s weights are fixed for a given checkpoint, but outputs can vary due to decoding choices (sampling, temperature) and nondeterministic runtime factors.
• Re-running with the same prompt and deterministic decoding yields the same output; sampling introduces deliberate variability.

Training and model behavior

• Model behavior emerges from hundreds of billions of learned parameters refined by gradient-based training (e.g., backpropagation) on large corpora.
• Post-training alignment (fine-tuning, RLHF) adjusts behavior toward safer or more helpful responses.

Practical tips

• Provide clear role markers and concise context.
• Include examples or desired formats in the prompt for consistent output.
• Use retrieval or tool calls for factual, up-to-date, or long-context needs.
• Adjust decoding settings to trade off creativity vs. predictability.