How Large Language Models Work: Read, Think, Talk, Train, and Use

This article explains how large language models (LLMs) process input, form internal representations, generate text, and are trained and adapted. It also gives practical guidance on prompt design, evaluation, common pitfalls, and an implementation checklist for reliable deployment.

• LLMs convert text into tokens and embeddings, reason with transformer attention, and generate text with controlled decoding methods.
• Training has stages: large-scale pretraining, then targeted fine-tuning and alignment (often via human feedback).
• Careful prompt design, decoding settings, and evaluation (automatic + human) are required to get reliable outputs.

Quick answer (one-paragraph summary)

LLMs read by tokenizing text and mapping tokens to numeric embeddings, they “think” using transformer layers that compute attention-based internal representations, and they “talk” by decoding those representations into tokens with sampling or beam methods; models are created by large-scale pretraining and then adapted via fine-tuning and alignment (such as RLHF), and reliable use requires careful prompt design, controlled decoding, and both automatic and human evaluation.

Explain how LLMs read: tokenization to embeddings

Reading begins by splitting input text into tokens — units that can be characters, subwords, or words depending on the tokenizer. Subword tokenizers (byte-pair encoding, WordPiece, or unigram) are common because they handle rare words and new compounds efficiently.

Each token is mapped to a numeric vector (embedding). Embeddings carry semantic and syntactic information the model learns during training, and position or segment encodings add order and context so the model can distinguish “dog bites man” from “man bites dog.”

Example: “unhappiness” might be tokenized as [“un”, “happi”, “ness”] so the model can reuse shared subword embeddings across words.

Explain how LLMs think: attention, transformers, and internal representations

Transformers are the primary architecture for modern LLMs. Each transformer layer uses self-attention to let every token attend to others, producing contextualized representations that change layer by layer.

Self-attention computes attention weights from queries, keys, and values derived from embeddings. Multi-head attention runs several attention mechanisms in parallel to capture different relation types (syntax, coreference, semantics).

Internal representations are the vectors produced at each token position and each layer. Lower layers often encode lexical features and local patterns; higher layers encode more abstract semantic and task-related properties. These internal vectors are what the decoder uses to produce the next-token probabilities.

Explain how LLMs talk: decoding, sampling, and response control

Decoding turns the model’s token probability distribution into a sequence of tokens. Common decoding strategies include greedy selection, beam search, and stochastic sampling methods that trade off creativity and determinism.

• Greedy: pick highest-probability token each step (fast but can be repetitive).
• Beam search: keep top sequences by cumulative probability (better coherence but less diverse).
• Sampling: random draw from distribution with controls — temperature, top-k, top-p (nucleus) — to increase diversity.

Control mechanisms: temperature adjusts distribution sharpness (low → conservative, high → creative); top-k limits to k most likely tokens; top-p restricts to smallest token set with cumulative probability p. Repetition penalties or token bans prevent loops or unsafe content. System messages, role prompts, and output-format constraints help shape responses.

Show how LLMs are trained and adapted: pretraining, fine-tuning, and RLHF

Training typically begins with self-supervised pretraining on large text corpora using next-token prediction or masked-token objectives. Pretraining teaches general language patterns and world knowledge at scale.

Fine-tuning adapts a pretrained model to a specific task using labeled examples (supervised fine-tuning) or task-specific data. Fine-tuning makes the model more accurate and aligned with desired outputs.

RLHF (Reinforcement Learning from Human Feedback) aligns models to human preferences: humans rate model outputs, a reward model is trained on those ratings, and reinforcement learning optimizes the policy (model) to maximize the reward while often maintaining fluency and safety.

Design prompts and instructions for reliable outputs

Prompt design converts user intent into inputs the model can reliably act on. Clear structure and constraints reduce ambiguity and improve repeatability.

• Start with a role/system instruction: “You are an assistant that…” to set tone and rules.
• Specify output format: “Return JSON with keys: title, summary, citations.” Use examples.
• Provide context and constraints: maximum length, forbidden content, audience level.
• Use few-shot examples to show desired inputs → outputs for complex tasks.
• Chain-of-thought: ask for reasoning steps only when you trust model confidentiality; it increases transparency but can leak heuristics or produce incorrect justifications.

Example prompt pattern: “You are an expert editor. Rewrite the following paragraph for a general audience in ≤50 words. Keep the facts unchanged.” Then include the paragraph. That pattern tells role, task, audience, and length constraint explicitly.

Evaluate and measure performance: metrics and human checks

Evaluation blends automatic metrics and human judgment. Automatic metrics allow scalable comparisons; human evaluation catches subtle failures, factual errors, and misuse risks.

Human evaluation should use clear rubrics, multiple raters per sample, and blinded comparisons when possible. For safety-critical systems, include targeted tests for hallucinations, toxic outputs, and privacy leaks.

Common pitfalls and how to avoid them

• Overreliance on default decoding: greedy or high-temperature outputs may be repetitive or unsafe. Remedy: tune temperature/top-p and use repetition penalties.
• Ambiguous prompts leading to wrong interpretation. Remedy: explicitly state format, constraints, and examples.
• Undetected hallucinations (confident but false assertions). Remedy: verify facts with grounded sources or add a retrieval step and require citations.
• Data distribution shift between training and application. Remedy: fine-tune on representative data or use few-shot examples matching target domain.
• Insufficient evaluation: metrics alone miss user-facing issues. Remedy: combine automatic metrics with systematic human evaluation and adversarial tests.
• Uncontrolled system changes: small prompt edits can change behavior unexpectedly. Remedy: version prompts, configs, and perform regression tests.

Implementation checklist

• Choose tokenizer and confirm tokenization behavior on target inputs.
• Select decoding strategy and tune temperature, top-k/top-p, and repetition controls.
• Define prompt templates with roles, constraints, and output formats; include examples.
• Decide on adaptation: supervised fine-tuning, adapters, or RLHF based on budget and risk.
• Establish evaluation plan: automatic metrics, human rubrics, and adversarial tests.
• Set monitoring for drift, safety incidents, and performance regression in production.

FAQ

Q: How do I reduce hallucinations in generated text?

A: Combine grounded retrieval (fetching verified sources), prompt constraints that require citations, lower temperature, and targeted fine-tuning on domain data. Use human review for high-stakes outputs.

Q: When should I fine-tune vs. use prompting/few-shot?

A: Use prompting/few-shot for low-cost, quick experiments or when you need flexibility. Fine-tune when you require consistent behavior at scale, specialized knowledge, or improved accuracy on labeled tasks.

Q: What decoding settings give the best balance of quality and creativity?

A: Start with temperature 0.7 and top-p 0.9 as a baseline, then adjust: lower temperature for factual tasks, higher for creative tasks. Evaluate results and tune based on human feedback.

Q: How do I measure model safety reliably?

A: Use a mix of automated classifiers for toxicity and privacy checks, systematic adversarial prompts, and diverse human raters with clear safety rubrics. Log incidents and retrain or update prompts when issues arise.