What is an LLM? How Large Language Models Work — 2026 Reference

TL;DR

One-line answer: A Large Language Model is the core engine of modern generative AI — a probabilistic predictor of language that, thanks to the Transformer architecture, captures meaning across long contexts.

• A Large Language Model (LLM) is a Transformer-based neural network trained on trillions of words to predict the next token probabilistically.
• Three core concepts explain everything: token (text unit), embedding (vector representing meaning), context window (the number of tokens the model can see at once).
• LLM training has three stages: pretraining (language), supervised fine-tuning (instruction following), RLHF/DPO (preference alignment).
• 2026 flagship models: GPT-5 (256K context, reasoning), Claude Opus 4.7 (1M context, code and agents), Gemini 3 (2M context, multimodal), Llama 4 (open-weight, self-hosted).
• Three ways to apply an LLM: prompt engineering (fastest), RAG (feed your own data), fine-tuning (to lock in style and behavior).

An LLM is a large neural network that has ingested trillions of text fragments to learn how to predict the next word. When the model is large enough and the data is rich enough, that predictive ability emerges as language understanding, reasoning, and generation.

Definition

Large Language Model (LLM)

A Transformer-based deep-learning model with billions of parameters, pretrained on internet-scale text corpora, capable of natural-language understanding, reasoning, and generation. It learns the probability of the next token; as scale grows, human-like language abilities emerge.

Also known as: LLM, Foundation Model

Wikidata: Q115305900

Important caveat: LLMs do not "think" or "understand" in a philosophical sense; they predict statistical probabilities at very large scale. Yet at sufficient scale, that ability produces outputs that behave like reasoning — a phenomenon known as emergent abilities.

At heart, an LLM is an autoregressive language model: it takes input, predicts the next most likely word (more precisely, token), appends it, predicts again. The loop continues until the response is complete.

A Simple Example

Given "The capital of France is...":

1. Tokenize the input
2. Convert each token into an embedding vector
3. Pass through Transformer layers to process context
4. Produce a probability distribution: " Paris" (87%), " Lyon" (4%), " a" (3%), ...
5. Pick the most likely token (or sample by temperature), append, repeat.

This simple mechanism, combined with trillions of tokens and billions of parameters, produces the reasoning, code-writing, translation, and summarization capabilities of modern LLMs.

Every LLM discussion centers on these three. You cannot ship without understanding them.

3.1. Token

The smallest text unit the model processes. A typical tokenizer splits text as:

• "machine learning" → ["machine", " learning"] — 2 tokens
• "Tokenization is hard" → ["Tok", "en", "ization", " is", " hard"] — 5 tokens

Practical implication: Morphologically rich languages (like Turkish, Finnish, Hungarian) consume 30-50% more tokens for the same content. API cost is higher; less content fits in the context window.

3.2. Embedding

Each token is mapped to a high-dimensional numerical vector. "cat" and "dog" embeddings sit close (both animals); "cat" and "mathematics" sit far apart. Embeddings are positions in a meaning space.

3.3. Context Window

The maximum number of tokens the model can "see" at once. 2026 flagship models:

2026 Context Window Comparison

Model	Context Window	Approx. English Words	Typical Use
GPT-4 (legacy)	8K-32K	~6,000-24,000	Short chat
GPT-5	256K	~200,000	Long report, codebase
Claude Opus 4.7	1M	~750,000	Full contract package, book
Gemini 3	2M	~1.5M	Video transcripts, multi-source
Llama 4 70B	128K	~95,000	Self-hosted RAG

"Long context solves everything" is wrong. Lost in the Middle effect (the model forgetting facts mid-context) still applies. Strategic retrieval + good prompt architecture usually beats brute-force long context.

Modern LLMs are built on the Transformer architecture introduced in Google's 2017 paper "Attention Is All You Need." Before that, models (RNN, LSTM) struggled with long-range dependencies.

Transformer Building Blocks

• Self-Attention: Each token "attends" to every other token in the sequence. This lets the model figure out, for example, what "it" refers to in "The manager read the report because it had to be presented tomorrow."
• Positional Encoding: Order information is encoded since tokens are a sequence.
• Multi-head Attention: Processes the same sentence through several relation types in parallel (syntactic, semantic, entity-relation).
• Feed-Forward Layers: Transform the attention output.
• Residual Connections + Layer Normalization: Stabilize deep stacking.

GPT-5, Claude, Gemini, Llama — all are Transformer variants; the differences lie in data, scale, training tricks, and alignment methods.

A modern LLM is trained in three stages, each adding a distinct capability.

How to

LLM Training — Three Stages

The path from raw model to production-ready LLM.

Total time: P6M

1. 1

   1. Pretraining

   Next-token prediction on trillions of tokens (Common Crawl, books, Wikipedia, code, academic texts). Months of GPU training, millions of dollars. Output: a base model with linguistic knowledge but no instruction-following ability.

2. 2

   2. Supervised Fine-tuning (SFT)

   Fine-tuning on thousands of high-quality Q&A pairs written by human annotators. Output: a model that follows instructions but is not yet aligned to preferences.

3. 3

   3. RLHF / DPO (Human Preference Alignment)

   Human-rated response pairs (A vs B) teach the model preferences. RLHF (Reinforcement Learning from Human Feedback) is the classic method; DPO (Direct Preference Optimization) is the more efficient modern alternative. Output: a production model aligned to be helpful, harmless, and honest.

At runtime (inference), several decisions matter:

Temperature

Controls randomness. 0 = deterministic (always the most likely token), 1 = creative, 2 = chaotic. Use 0-0.2 for extraction, 0.7-1.0 for creative writing.

Top-p (Nucleus Sampling)

Select among the tokens whose cumulative probability reaches p. Often tuned alongside temperature.

Max Tokens

Caps output length. Critical for cost and latency.

Stop Sequences

Special strings that end generation (e.g., "###", "User:").

2026 Flagship LLMs

Model	Provider	Context	Strength	Typical Cost (per 1M tokens)
GPT-5	OpenAI	256K	Reasoning chain, OpenAI ecosystem	$5-15
Claude Opus 4.7	Anthropic	1M	Long context, code, agent use	$15-75
Gemini 3	Google	2M	Multimodal (video+audio+image), Google ecosystem	$3-10
Llama 4 70B	Meta (open)	128K	Self-hosted, free weights	$0.20-2 (self-hosted)
Mistral Large 3	Mistral	128K	European, GDPR-friendly	$2-8
DeepSeek V3	DeepSeek (open)	128K	Low cost, MoE architecture	$0.30-1
Qwen 2.5	Alibaba (open)	128K	Multilingual	$0.50-2

Which One for What?

• Complex reasoning + agent workflows: Claude Opus 4.7
• General chat + creative content: GPT-5 or Claude
• Video/audio understanding: Gemini 3
• Cost-critical high volume: GPT-4o-mini, Claude Haiku, Gemini Flash, DeepSeek
• Data residency / compliance: Mistral (EU), self-hosted Llama / Qwen (on-prem)

Know the limits before designing production systems.

8.1. Hallucination

LLMs do not know what they do not know; they can produce confident-sounding but wrong answers. The model alone does not solve this — RAG, citations, eval harness, and human review are required.

8.2. Knowledge Cutoff

Every LLM has a training-data cutoff and does not know events afterward. RAG or web search is required for post-cutoff facts.

8.3. Mathematical Reasoning

Weak on arithmetic and symbolic reasoning (especially long computations). Solution: tool use (calculator, Python execution) or chain-of-thought prompting.

8.4. Real-Time Data

LLMs do not know live data (stock prices, weather, news) on their own. Tool use / function calling is essential.

8.5. Character-Level Tasks

Surprisingly weak at counting letters or words — because models work on tokens, character-level reasoning is the exception, not the norm.

LLM and Other AI Model Types

Model Type	Task	Examples	Relation to LLM
LLM (Language Model)	Understand and generate text	GPT-5, Claude, Gemini	Subject of this article
Diffusion Model	Generate image / video	Stable Diffusion, Flux, Sora	Different architecture (denoising)
Embedding Model	Produce meaning vectors	BGE-M3, OpenAI text-embedding	Related architecture, smaller
Speech Model	ASR / TTS	Whisper, ElevenLabs	Different (audio-specific)
Vision Model	Image understanding	CLIP, ResNet, ViT	Integrated into multimodal LLMs
Multimodal LLM	Text + image + audio + video	GPT-5, Gemini 3, Claude Opus	Combines multiple modalities in one model

Three foundational approaches to tailor an LLM to your use case.

10.1. Prompt Engineering (Fastest)

Steer the model's existing capabilities with a good instruction. Few-shot examples, chain-of-thought, system-prompt design fall here. Low cost, deploy in hours.

10.2. RAG — Retrieval-Augmented Generation (Medium)

Fetch your company's data from a knowledge base and append to the prompt. The right approach for any use case involving a knowledge base + fresh data. Medium cost, weeks/months to production.

10.3. Fine-tuning (Heaviest)

Train the model on extra data to change behavior/style. LoRA, QLoRA, DPO reduce GPU cost. Use when you must lock in a specific tone or specialize in a closed domain. High cost, can take months.

Turkish is morphologically rich — each word can have dozens of inflected forms. This makes Turkish LLM performance sensitive to tokenizer efficiency and training-data share.

2026 Turkish LLM Landscape

• Strongest: Claude Opus 4.7, GPT-5, Gemini 3 — all three near-native fluency
• Good: Mistral Large 3, GPT-4o, DeepSeek V3
• Moderate: Llama 4 70B (instruct), Qwen 2.5 72B
• Local: Cezeri, KanarYa, Trendyol-LLM (e-commerce-specialized), BERTurk (NLP research)

Factors Affecting Turkish Performance

1. Tokenizer efficiency. Tokenizers that fragment Turkish less use the context window better.
2. Turkish data share in training. In the largest models, Turkish content typically sits around 1-3%; even that can deliver fluency.
3. Domain specificity. Legal, medical, and finance vocabularies benefit from Turkish-domain fine-tuning in enterprise projects.

LLM costs are token-based. The cost of an API call has three parts:

1. Input token (prompt) cost — what you send
2. Output token (response) cost — what the model generates (typically 2-3x more expensive)
3. Cached token cost — reused prompts (50-90% discount via prompt caching)

Typical Monthly Cost Scenarios (2026 Pricing)

• Small internal chatbot (10K queries/month, GPT-4o-mini): ~$50-150
• Mid enterprise RAG (50K queries/month, GPT-5 + RAG): ~$1,500-5,000
• Large customer service (500K queries/month, Claude Opus + Haiku mix): ~$8,000-30,000
• Self-hosted Llama 70B (fixed GPU, usage-independent): ~$2,000-5,000/month (incl. hardware amortization)

Cost Optimization

• Prompt caching: 50-90% savings on repeated system prompts
• Model routing: Simple queries to small models, complex ones to large
• Response caching: Cache full responses for frequent questions
• Streaming: Cuts perceived latency in half, improves UX
• Batch API: 50% discount for async workloads (24-hour turnaround)

To shape LLM strategy in your company or harden an existing application to production quality:

1. LLM selection workshop. The most suitable model (quality + cost + data residency) for your use case clarified in one session.
2. RAG architecture workshop. End-to-end design to combine your company's data with LLMs.
3. Production audit. If you already have an LLM application: 360° audit for hallucination, latency, cost, and compliance.

Reach out via the contact form on the site.

References

1. Attention Is All You Need — Vaswani et al., NeurIPS · 2017-06-12
2. Language Models are Few-Shot Learners (GPT-3) — Brown et al., NeurIPS · 2020-05-28
3. Training language models to follow instructions with human feedback (InstructGPT/RLHF) — Ouyang et al., OpenAI · 2022-03-04
4. Constitutional AI: Harmlessness from AI Feedback — Bai et al., Anthropic · 2022-12-15
5. Direct Preference Optimization (DPO) — Rafailov et al., NeurIPS · 2023-05-29
6. Lost in the Middle: How Language Models Use Long Contexts — Liu et al., arXiv · 2023-07-06
7. Emergent Abilities of Large Language Models — Wei et al., TMLR · 2022-06-15
8. GPT-4 Technical Report — OpenAI, OpenAI · 2023-03-15
9. Stanford AI Index Report 2025 — Stanford HAI, Stanford University · 2025-04
10. State of AI Report 2025 — Benaich, N., Air Street Capital · 2025-10

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This is a living document; the LLM ecosystem (new models, pricing, architectural updates) shifts every quarter, so it is updated quarterly.