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Open source · Python

← Guides> Library> Topic

LLM mental models: ways to think about language models

A mental model is a simple picture of how something works that helps you predict what it will do. Language models are hard to reason about directly, so each guide in this series offers one picture and shows what follows from it for prompting.

Seen as a probability map, a model picks likely continuations, not true ones. Seen as a simulator, it plays whatever role and world the prompt sets up. Seen as a trickster, it is fluent and sometimes wrong, so you generate and then verify. No single picture is complete; together they explain why clear context, examples, verification and limits work. Each essay starts from a question people ask about LLMs, explains it with one picture, and can be read on its own.

[n.01/02]> 19 guides
  1. 01Why Can't LLMs Count Letters? Tokens and ProbabilityWhy LLMs miscount letters, reverse strings wrong and miss word limits: tokenization, sampling, real failures, and why exact work belongs in your code.Beginner · 4 minOct 18, 2026
  2. 02How to Brief an LLM: Context Gaps and Clarifying QuestionsHow to brief an LLM like a new colleague: why it lacks your context and won't ask, the jagged frontier, and a real example of guesses swapped for asks.Beginner · 5 minOct 18, 2026
  3. 03Are LLMs Stochastic Parrots? What the Term Gets RightAre LLMs stochastic parrots? What Bender et al. meant, what next-token prediction implies, where the term falls short, and the habit it should give you.Beginner · 5 minOct 18, 2026
  4. 04Can LLMs Simulate Users? Role-Play, Personas and LimitsCan LLMs simulate users or customers? The simulator view of LLMs, what research says about personas, two real role-plays that went wrong, and fixes.Intermediate · 5 minOct 18, 2026
  5. 05Does AI Boost Productivity? The LLM as Cognitive MultiplierDoes AI make you more productive? What field studies show about the jagged frontier, and how to use an LLM to sharpen your judgment. Real plan example.Intermediate · 5 minOct 18, 2026
  6. 06How LLMs Generate Text: Next-Token Probabilities ExplainedHow LLMs generate text one token at a time: probabilities, temperature, top-p, and why early words commit the rest. Real example of a verdict-first error.Intermediate · 5 minOct 18, 2026
  7. 07Why LLM Output Is Untrusted Input: Design for InjectionWhy to treat LLM output as untrusted: indirect prompt injection, OWASP LLM risks, a real injected support ticket, and design rules that contain the damage.Intermediate · 5 minOct 18, 2026
  8. 08Why LLMs Mirror You: Sycophancy, Bias and Leading PromptsWhy LLMs agree with you: sycophancy from human-feedback training and bias from human text. Prompts that get honest feedback, with a real rating example.Intermediate · 5 minOct 18, 2026
  9. 09How LLM Tool Calling Works: The Model Never Runs ToolsHow LLM tool calling works: the model writes a request, your code runs it. A real two-step refund exchange and design rules for tools, results and gates.Intermediate · 4 minOct 18, 2026
  10. 10Prompts as Code: The LLM as a Probabilistic CompilerTreat prompts as code: write specs, enforce output shape with structured outputs, surface assumptions and test every change. Real date-extraction example.Intermediate · 5 minOct 18, 2026
  11. 11Prompts as Interfaces: Design Prompts Like API ContractsDesign prompts like API contracts: delimited inputs, a fixed output, a defined failure path, versions and tests. A real classifier prompt before and after.Intermediate · 4 minOct 18, 2026
  12. 12LLM as a Judge vs a Jury: Making AI Evaluations ReliableLLM-as-a-judge explained: position, length and self-preference bias, and how a jury of swapped orders, rubrics and mixed models fixes it. Real example.Intermediate · 5 minOct 18, 2026
  13. 13Why LLMs Fail Modified Puzzles: Pattern-Matching BiasWhy LLMs answer the famous version of a changed puzzle: training-data priors, research on counterfactual tasks, a real failure and how to prompt around it.Intermediate · 5 minOct 18, 2026
  14. 14How Context Shapes LLM Output: The Model Continues ItWhy LLMs copy the patterns in your context, flaws included: next-token prediction, Codex and multi-turn research, a real example and three fixes.Intermediate · 4 minOct 18, 2026
  15. 15Why LLMs Get Niche Facts Wrong: Popularity and Training DataWhy LLMs know popular facts but invent niche ones: accuracy tracks how often a fact appears in training data. When to retrieve instead. Real example.Intermediate · 5 minOct 18, 2026
  16. 16AI Code Hallucinations: Why Plausible Output Fools YouWhy LLM code looks right but fails: plausibility vs correctness, hallucinated packages, a real regex that broke, and how to verify output by running it.Intermediate · 4 minOct 18, 2026
  17. 17Are LLMs Black Boxes? What You Can and Can't InspectAre LLMs black boxes? Why model explanations can hide the real cause, and what you can inspect instead: context, tool calls, counterfactual tests.Intermediate · 5 minOct 18, 2026
  18. 18LLMs as Lossy Compression: Why They Can't Quote ExactlyWhy an LLM is a lossy compression of its training data: it keeps gist and loses exact wording, quotes and URLs. What to ask it for, with a real example.Intermediate · 4 minOct 18, 2026
  19. 19Why LLMs Give Similar Answers (and How to Get Variety)Why LLMs repeat the same kind of answer: mode collapse after RLHF. Get real variety with sets, named axes and verbalized sampling. Real naming example.Intermediate · 5 minOct 18, 2026
[n.02/02]> go further

Related topics

  • How LLMs Work8
  • Hallucinations11
  • Evals and Testing15
  • Grounding and Citations6
  • Guardrails9
  • Tool Calling16
  • Context Engineering6
  • LLM-as-a-Judge3

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