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

> Source: https://sukruyusufkaya.com/en/blog/llm-nedir-buyuk-dil-modelleri
> Updated: 2026-05-13T19:58:05.215Z
> Type: blog
> Category: yapay-zeka
**TLDR:** How do Large Language Models (LLMs) work, what does Transformer architecture solve, what are tokens, embeddings, and context windows, and how do GPT-5, Claude Opus 4.7, Gemini 3, and Llama 4 compare? A comprehensive 2026 reference covering Turkish LLM performance, training stages, hallucination control, and cost modeling.

<tldr data-summary="[&#34;A Large Language Model (LLM) is a Transformer-based neural network trained on trillions of words to predict the next token probabilistically.&#34;,&#34;Three core concepts explain everything: token (text unit), embedding (vector representing meaning), context window (the number of tokens the model can see at once).&#34;,&#34;LLM training has three stages: pretraining (language), supervised fine-tuning (instruction following), RLHF/DPO (preference alignment).&#34;,&#34;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).&#34;,&#34;Three ways to apply an LLM: prompt engineering (fastest), RAG (feed your own data), fine-tuning (to lock in style and behavior).&#34;]" data-one-line="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."></tldr>

## 1. What is an LLM? The One-Sentence Answer

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-box data-term="Large Language Model (LLM)" data-definition="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." data-also="LLM, Foundation Model" data-wikidata="Q115305900"></definition-box>

**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*.

## 2. How an LLM Works — A Prediction Machine

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.

## 3. Three Core Concepts: Token, Embedding, Context Window

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**.

<callout-box data-variant="answer" data-title="What are embeddings used for?">

Embeddings are the foundation of RAG (Retrieval-Augmented Generation). The embedding of a document is compared to the embedding of a query to find relevant documents. Without embeddings, modern semantic search, recommendation, and RAG cannot work.

</callout-box>

### 3.3. Context Window

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

<comparison-table data-caption="2026 Context Window Comparison" data-headers="[&#34;Model&#34;,&#34;Context Window&#34;,&#34;Approx. English Words&#34;,&#34;Typical Use&#34;]" data-rows="[{&#34;feature&#34;:&#34;GPT-4 (legacy)&#34;,&#34;values&#34;:[&#34;8K-32K&#34;,&#34;~6,000-24,000&#34;,&#34;Short chat&#34;]},{&#34;feature&#34;:&#34;GPT-5&#34;,&#34;values&#34;:[&#34;256K&#34;,&#34;~200,000&#34;,&#34;Long report, codebase&#34;]},{&#34;feature&#34;:&#34;Claude Opus 4.7&#34;,&#34;values&#34;:[&#34;1M&#34;,&#34;~750,000&#34;,&#34;Full contract package, book&#34;]},{&#34;feature&#34;:&#34;Gemini 3&#34;,&#34;values&#34;:[&#34;2M&#34;,&#34;~1.5M&#34;,&#34;Video transcripts, multi-source&#34;]},{&#34;feature&#34;:&#34;Llama 4 70B&#34;,&#34;values&#34;:[&#34;128K&#34;,&#34;~95,000&#34;,&#34;Self-hosted RAG&#34;]}]"></comparison-table>

"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.

## 4. The Transformer Architecture: 2017's Revolution

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.

## 5. Training Stages: How an LLM is Born

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

<howto-steps data-name="LLM Training — Three Stages" data-description="The path from raw model to production-ready LLM." data-time="P6M" data-steps="[{&#34;name&#34;:&#34;1. Pretraining&#34;,&#34;text&#34;:&#34;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.&#34;},{&#34;name&#34;:&#34;2. Supervised Fine-tuning (SFT)&#34;,&#34;text&#34;:&#34;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.&#34;},{&#34;name&#34;:&#34;3. RLHF / DPO (Human Preference Alignment)&#34;,&#34;text&#34;:&#34;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.&#34;}]"></howto-steps>

<callout-box data-variant="tip" data-title="Why Constitutional AI Matters">

Anthropic's Constitutional AI approach has the model critique and improve its own responses against a written set of principles. It is the method behind the high safety and transparency scores of the Claude family, and a scalable answer to the alignment problem RLHF alone cannot solve.

</callout-box>

## 6. Inference: What Happens When an LLM Answers?

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:").

## 7. 2026 Flagship LLM Comparison

<comparison-table data-caption="2026 Flagship LLMs" data-headers="[&#34;Model&#34;,&#34;Provider&#34;,&#34;Context&#34;,&#34;Strength&#34;,&#34;Typical Cost (per 1M tokens)&#34;]" data-rows="[{&#34;feature&#34;:&#34;GPT-5&#34;,&#34;values&#34;:[&#34;OpenAI&#34;,&#34;256K&#34;,&#34;Reasoning chain, OpenAI ecosystem&#34;,&#34;$5-15&#34;]},{&#34;feature&#34;:&#34;Claude Opus 4.7&#34;,&#34;values&#34;:[&#34;Anthropic&#34;,&#34;1M&#34;,&#34;Long context, code, agent use&#34;,&#34;$15-75&#34;]},{&#34;feature&#34;:&#34;Gemini 3&#34;,&#34;values&#34;:[&#34;Google&#34;,&#34;2M&#34;,&#34;Multimodal (video+audio+image), Google ecosystem&#34;,&#34;$3-10&#34;]},{&#34;feature&#34;:&#34;Llama 4 70B&#34;,&#34;values&#34;:[&#34;Meta (open)&#34;,&#34;128K&#34;,&#34;Self-hosted, free weights&#34;,&#34;$0.20-2 (self-hosted)&#34;]},{&#34;feature&#34;:&#34;Mistral Large 3&#34;,&#34;values&#34;:[&#34;Mistral&#34;,&#34;128K&#34;,&#34;European, GDPR-friendly&#34;,&#34;$2-8&#34;]},{&#34;feature&#34;:&#34;DeepSeek V3&#34;,&#34;values&#34;:[&#34;DeepSeek (open)&#34;,&#34;128K&#34;,&#34;Low cost, MoE architecture&#34;,&#34;$0.30-1&#34;]},{&#34;feature&#34;:&#34;Qwen 2.5&#34;,&#34;values&#34;:[&#34;Alibaba (open)&#34;,&#34;128K&#34;,&#34;Multilingual&#34;,&#34;$0.50-2&#34;]}]"></comparison-table>

### 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)

## 8. LLM Limits: What They Cannot Do

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.

<stat-callout data-value="23%" data-context="According to the 2025 Stanford AI Index, hallucination rates of large LLMs on certain Turkish geographic/historical queries" data-outcome="can reach a meaningful share of unverified generations." data-source="{&#34;label&#34;:&#34;Stanford AI Index 2025&#34;,&#34;url&#34;:&#34;https://aiindex.stanford.edu/&#34;,&#34;date&#34;:&#34;2025&#34;}"></stat-callout>

### 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.

## 9. LLM vs Other AI Model Types

<comparison-table data-caption="LLM and Other AI Model Types" data-headers="[&#34;Model Type&#34;,&#34;Task&#34;,&#34;Examples&#34;,&#34;Relation to LLM&#34;]" data-rows="[{&#34;feature&#34;:&#34;LLM (Language Model)&#34;,&#34;values&#34;:[&#34;Understand and generate text&#34;,&#34;GPT-5, Claude, Gemini&#34;,&#34;Subject of this article&#34;]},{&#34;feature&#34;:&#34;Diffusion Model&#34;,&#34;values&#34;:[&#34;Generate image / video&#34;,&#34;Stable Diffusion, Flux, Sora&#34;,&#34;Different architecture (denoising)&#34;]},{&#34;feature&#34;:&#34;Embedding Model&#34;,&#34;values&#34;:[&#34;Produce meaning vectors&#34;,&#34;BGE-M3, OpenAI text-embedding&#34;,&#34;Related architecture, smaller&#34;]},{&#34;feature&#34;:&#34;Speech Model&#34;,&#34;values&#34;:[&#34;ASR / TTS&#34;,&#34;Whisper, ElevenLabs&#34;,&#34;Different (audio-specific)&#34;]},{&#34;feature&#34;:&#34;Vision Model&#34;,&#34;values&#34;:[&#34;Image understanding&#34;,&#34;CLIP, ResNet, ViT&#34;,&#34;Integrated into multimodal LLMs&#34;]},{&#34;feature&#34;:&#34;Multimodal LLM&#34;,&#34;values&#34;:[&#34;Text + image + audio + video&#34;,&#34;GPT-5, Gemini 3, Claude Opus&#34;,&#34;Combines multiple modalities in one model&#34;]}]"></comparison-table>

## 10. Three Ways to Adapt an LLM

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.

<callout-box data-variant="tip" data-title="Decision Framework">

About 70% of needs are met by **prompt engineering**; 25% more require **RAG**; only ~5% of cases produce real value from **fine-tuning**. Start simple, look at eval, then add complexity. Most projects that begin with "let's fine-tune" would have been solved by prompt + RAG anyway.

</callout-box>

## 11. Turkish LLM Performance

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)

<callout-box data-variant="answer" data-title="For Turkish: OpenAI, Claude, or Gemini?">

As of 2026, **all three perform at near-native level** in Turkish. Differences are task-based: **Claude for code and agents**, **Gemini for multimodal and video**, **GPT for OpenAI-ecosystem integration**. There is no single right answer; test against your own eval set.

</callout-box>

### 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.

## 12. LLM Cost Model

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)

## 13. Frequently Asked Questions

<callout-box data-variant="answer" data-title="Is an LLM the same as a chatbot?">

No. **An LLM** is a model type (e.g., GPT-5); **a chatbot** is an application format. ChatGPT is a chatbot application running GPT-5 (and others) under the hood. The same LLM can serve different interfaces (API, IDE assistant, agent, RAG system).

</callout-box>

<callout-box data-variant="answer" data-title="Does an LLM really 'understand'?">

Philosophically debated. Behaviorally, LLMs exhibit human-like skills (reasoning, translation, summarization), yet the internal mechanism is statistical prediction. "Does it understand?" reaches Searle's Chinese Room; practically, **does the output work** is a more useful test.

</callout-box>

<callout-box data-variant="answer" data-title="Open-source LLM or closed API?">

Three criteria: **(1)** Data sensitivity high? → open-source self-hosted (Llama, Qwen, DeepSeek), **(2)** Need top quality? → closed API (GPT-5, Claude Opus, Gemini 3), **(3)** Cost-first? → depends on volume: small means API, large means run the self-hosted math. Most enterprise projects end up hybrid.

</callout-box>

<callout-box data-variant="answer" data-title="Should I train my own LLM?">

Almost certainly not. Training from scratch costs millions and takes months; current open-weight models (Llama, Qwen) are already strong. What you might do is **fine-tune** (weeks via LoRA/QLoRA, thousands of dollars) — but first try prompt + RAG.

</callout-box>

<callout-box data-variant="answer" data-title="How do I prevent the LLM from making mistakes?">

Errors do not go to zero — this is a probabilistic system. But four layers control it: **(1)** RAG with source-grounded answers, **(2)** Permission in the system prompt to say "I don't know", **(3)** Eval harness for continuous measurement, **(4)** Human-in-the-loop for high-stakes decisions. Do not ship without all four.

</callout-box>

<callout-box data-variant="answer" data-title="As context windows grow, won't RAG become obsolete?">

No. The lost-in-the-middle effect means models often forget facts in the middle of a long context, and long context is billed per query. **Strategic retrieval (RAG) + good prompt architecture** is usually both more accurate and cheaper than brute-loading a long context.

</callout-box>

<callout-box data-variant="answer" data-title="Why doesn't the LLM give the same answer twice?">

Because the inference temperature adds randomness. For deterministic answers, use <code>temperature: 0</code> and a fixed seed. Production typically prefers 0-0.3.

</callout-box>

<callout-box data-variant="answer" data-title="Are GPT-5 and ChatGPT the same?">

No. **GPT-5 is the model**, **ChatGPT is the app**. ChatGPT runs GPT-4o, GPT-5, and other models; OpenAI updates the app continuously. Similarly, Claude.ai runs Claude Sonnet/Opus models.

</callout-box>

<callout-box data-variant="answer" data-title="Can LLMs be used legally in Turkey?">

Yes, under KVKK and EU AI Act compliance. Personal data in prompts requires anonymization, cross-border-transfer controls, and transparency obligations. A separate compliance guide on this site covers the full framework.

</callout-box>

## 14. Next Steps

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.

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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**.