How LLMs Work: Tokens, Context, Limitations, and Bugs

Table Of Contents

• What Changed in LLMs in 2026
• Tokens: The Fundamental Unit of LLMs
  • How Tokenization Works
  • Why Tokens Matter for Your Budget
• Context Windows: What the Model Can "See"
  • Context Window Sizes (March 2026)
  • The "Lost in the Middle" Problem
  • Practical Context Management
• How LLMs Generate Text: Prediction, Not Understanding
  • The Prediction Process
  • Temperature: Controlling Randomness
• Common LLM Limitations and Bugs
  • 1. Hallucinations
  • 2. Mathematical Errors
  • 3. Reasoning Failures
  • 4. Context Window Degradation
  • 5. Sycophancy
• Practical Tips for Working With LLMs
  • Prompt Structure That Gets Better Results
  • Common Prompt Mistakes
  • When to Use ChatGPT vs Claude vs Gemini
• Quick Start Checklist
• What to Read Next

Updated: April 2026

TL;DR: Large Language Models (LLMs) like ChatGPT and Claude process text as tokens, not words — and this fundamental mechanic explains most of their limitations. Understanding tokens, context windows, and common failure modes helps you get better outputs and avoid costly mistakes. OpenAI's ChatGPT now serves 900+ million weekly users (OpenAI, 2026), but most users don't understand why the model fails when it does. If you need AI accounts for work right now — catalog with instant delivery.

A Large Language Model (LLM) is a neural networktrained on massive text datasets to predict the most likely next token in a sequence. It does not "understand" language the way humans do — it generates statistically probable continuations of your input. This distinction explains every limitation, bug, and surprising capability of modern AI.

What Changed in LLMs in 2026

• OpenAI's ChatGPT reached 900+ million weekly users and $12.7 billion ARR (OpenAI/Bloomberg, March 2026)
• Claude's context window expanded to 200K tokens — roughly 150,000 words in a single conversation (Anthropic, 2025)
• GPT-4 Turbo reduced token costs by 3x while maintaining quality, making LLMs viable for high-volume production use
• According to Bloomberg (2025), the generative AI market hit $67 billion — driven primarily by LLM adoption
• Multi-modal LLMs (text + image + audio understanding) became standard rather than experimental

Tokens: The Fundamental Unit of LLMs

LLMs don't read words — they read tokens. A token is a chunk of text that the model processes as a single unit. Understanding tokenization is essential for effective AI use.

How Tokenization Works

Key rules: - 1 token ≈ 4 characters in English, roughly 0.75 words - Non-English languages use more tokens per word — Russian text costs ~1.5-2x more tokens than English - Numbers and special characters are expensive — a URL can use 10-20 tokens - Rare words get split into more tokens than common words

Why Tokens Matter for Your Budget

Every API call costs money per token — both input (your prompt) and output (the response). If you're using AI at scale for content generation, understanding tokens directly impacts costs.

Related: Multimodal AI Models: Text, Images and Video — Real Scenarios, Limits and What Actually Works

A 2,000-word article in English is roughly 2,700 tokens. At GPT-4o prices, generating one article costs about $0.03 in output tokens. But if your prompt includes a long system message, examples, and context — input tokens can cost 5-10x more than the output.

Case: Content agency generating 100 articles/month using GPT-4 API. Problem: Monthly API costs reached $450 due to long prompts with repeated instructions and context in every request. Action: Restructured prompts — moved static instructions to system message, shortened examples, cached frequently used context. Switched long-form generation to Claude 3.5 Sonnet for better quality-per-token. Result: Monthly API costs dropped to $120 while output quality improved. Key insight: shorter, more precise prompts produce better results AND cost less.

⚠️ Important: Token limits are hard limits. When your conversation exceeds the context window, the model silently drops earlier messages — it doesn't warn you. This means the model can forget your initial instructions mid-conversation. For long projects, re-state critical instructions periodically or use the API with explicit context management.

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Context Windows: What the Model Can "See"

The context window is the maximum amount of text an LLM can process in a single interaction — including both your input and the model's output.

Context Window Sizes (March 2026)

The "Lost in the Middle" Problem

Research shows that LLMs pay most attention to the beginning and end of their context window, and less to information in the middle. This means:

• Put your most important instructions at the start of the prompt
• Put critical data at the start or end of long documents
• Don't assume the model processes everything equally — it doesn't
• For long documents, summarize key points and place them at the top

Practical Context Management

For media buyers and marketers working on long projects:

Related: Prompt Engineering: Query Structures, Roles, Restrictions, and Practical Examples

1. Break long tasks into chunks — don't try to generate a 5,000-word article in one prompt
2. Re-state key instructions when starting new sections
3. Use structured prompts with clear headers — models navigate structure better than continuous text
4. Keep conversations focused — start new chats for new tasks rather than extending old ones

How LLMs Generate Text: Prediction, Not Understanding

LLMs work by predicting the next token based on all previous tokens. This is fundamentally different from understanding meaning.

The Prediction Process

1. Your input is tokenized
2. Tokens pass through transformer layers that calculate relationships between all tokens
3. The model outputs a probability distribution over all possible next tokens
4. A sampling strategy picks one token (temperature controls randomness)
5. Steps 2-4 repeat until the output is complete

Temperature: Controlling Randomness

Temperature controls how random the model's choices are:

For marketing tasks, temperature 0.3-0.7 usually works best. Lower for factual content, higher for creative variations.

Related: LLM Security: Prompt Injection, Data Leaks, and Instruction Protection

Case: Media buyer using ChatGPT API for generating 50 ad headline variations per day. Problem: Headlines were too similar — all followed the same pattern, limiting A/B test effectiveness. Action: Increased temperature from 0.3 to 0.8, added diverse prompt templates, and used "generate 10 headlines using completely different angles" instruction. Result: Headline diversity increased by 4x. A/B test win rate improved from 12% to 31% as the model explored more creative territory. Top-performing headlines came from the higher-temperature runs 60% of the time.

Common LLM Limitations and Bugs

1. Hallucinations

The most well-known bug. LLMs generate plausible-sounding but factually wrong information. This happens because the model optimizes for text that "sounds right" based on patterns, not for factual accuracy.

Most common hallucination types: - Fabricated statistics with fake sources - Non-existent research papers cited by author name and title - Incorrect technical specifications for real products - Made-up company policies or features - Wrong dates for historical events

Mitigation: Always verify factual claims. Use Claude's citation features when available. Cross-check with a second model.

2. Mathematical Errors

LLMs are surprisingly bad at math. They predict the most likely next token, not the correct mathematical result. Simple arithmetic can fail unpredictably.

Examples of common math failures: - Multiplication of large numbers (e.g., 347 × 891) - Percentage calculations with multiple steps - Currency conversions with non-round exchange rates - Compound interest calculations

Mitigation: Use ChatGPT's Code Interpreter for any calculations. It runs actual Python code rather than predicting the answer.

3. Reasoning Failures

LLMs can follow logical chains but frequently fail at multi-step reasoning, especially when intermediate steps require holding multiple conditions in memory.

Common reasoning failures: - Contradicting earlier statements in long outputs - Failing to apply stated constraints consistently - "Forgetting" exceptions mentioned earlier in the prompt - Circular logic — using the conclusion as evidence

Mitigation: Break complex reasoning into explicit steps. Ask the model to "think step by step" and verify each step.

4. Context Window Degradation

As conversations get longer, model quality degrades. The model pays less attention to earlier messages and may lose track of instructions.

Mitigation: Start new conversations for new tasks. Re-state critical context periodically. Use the API with explicit context management for production workflows.

5. Sycophancy

LLMs tend to agree with the user rather than push back on incorrect statements. If you say "the sky is green, right?" many models will agree or equivocate rather than correct you clearly.

Mitigation: Frame questions neutrally. Instead of "This is good copy, right?" ask "What are the strengths and weaknesses of this copy?"

⚠️ Important: LLM limitations compound in production. A hallucinated statistic (limitation 1) can be reinforced by sycophancy (limitation 5) when you ask the model to verify its own output. Always use a different model or manual verification for fact-checking — never ask the same model to verify its own claims.

Practical Tips for Working With LLMs

Prompt Structure That Gets Better Results

Role: [Who the model should act as]
Task: [What you want it to do]
Context: [Background information]
Format: [How to structure the output]
Constraints: [What to avoid]
Examples: [1-2 examples of desired output]

Common Prompt Mistakes

When to Use ChatGPT vs Claude vs Gemini

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Quick Start Checklist

• [ ] Learn how your main AI tool tokenizes text (use OpenAI's tokenizer tool or tiktoken)
• [ ] Calculate your monthly token costs and optimize prompts for efficiency
• [ ] Set up a prompt template library with role, task, context, format, and constraints
• [ ] Test temperature settings for your most common tasks (lower for facts, higher for creativity)
• [ ] Create a verification workflow for all AI outputs containing specific claims
• [ ] Start new conversations for new tasks — don't chain unrelated work
• [ ] Never ask a model to verify its own output — use a second model or manual check

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What to Read Next

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