Aug 30, 2025·8 min
A Simple Mental Model of How AI Thinks When Building Apps
A clear mental model for how AI generates code and decisions in apps—tokens, context, tools, and tests—plus limits and practical prompting tips.
A clear mental model for how AI generates code and decisions in apps—tokens, context, tools, and tests—plus limits and practical prompting tips.
When people say “AI thinks,” they usually mean something like: it understands your question, reasons about it, and then decides on an answer.
For modern text-based AI (LLMs), a more useful mental model is simpler: the model predicts what text should come next.
That might sound underwhelming—until you see how far “next text” can go. If the model has learned enough patterns from training, predicting the next word (and the next, and the next) can produce explanations, plans, code, summaries, and even structured data that your app can use.
You don’t need to learn the underlying math to build good AI features. What you do need is a practical way to anticipate behavior:
This article is that kind of model: not hype, not a deep technical paper—just the concepts that help you design reliable product experiences.
From an app builder’s perspective, the model’s “thinking” is the text it generates in response to the input you provide (your prompt, user messages, system rules, and any retrieved content). The model is not checking facts by default, not browsing the web, and not “knowing” what your database contains unless you pass that information in.
Set expectations accordingly: LLMs are incredibly useful for drafting, transforming, and classifying text, and for generating code-like outputs. They are not magical truth engines.
We’ll break the mental model into a few parts:
With these ideas, you can design prompts, UI, and safeguards that make AI features feel consistent and trustworthy.
When people say an AI “thinks,” it’s easy to imagine it reasoning the way a person does. A more useful mental model is simpler: it’s doing extremely fast autocomplete—one small piece at a time.
A token is a chunk of text the model works with. Sometimes it’s a whole word (“apple”), sometimes part of a word (“app” + “le”), sometimes punctuation, and sometimes even whitespace. The exact chunking depends on the model’s tokenizer, but the takeaway is: the model doesn’t process text as neat sentences—it processes tokens.
The model’s core loop is:
That’s it. Every paragraph, bullet list, and “reasoning” chain you see is built from repeating this next-token prediction many times.
Because the model has seen massive amounts of text during training, it learns patterns like how explanations usually flow, what a polite email sounds like, or how a bug fix is typically described. When you ask a question, it generates an answer that fits the patterns it has learned and matches the context you provided.
This is why it can sound confident and coherent even when it’s wrong: it’s optimizing for what text should come next—not for checking reality.
Code is not special to the model. JavaScript, SQL, JSON, and error messages are all just sequences of tokens. The model can produce useful code because it has learned common coding patterns, not because it truly “understands” your app the way an engineer on your team would.
When people ask “where did the model get that answer?”, the most useful mental model is: it learned patterns from a huge number of examples, and now it recombines those patterns to predict what text should come next.
During training, the model is shown many text snippets (books, articles, code, documentation, Q&As, and more). It repeatedly practices a simple task: given some text, predict the next token. When it gets the prediction wrong, the training process nudges the model’s internal parameters so it’s slightly more likely to predict a better next token next time.
Over time, those nudges add up. The model starts to encode relationships like:
Because it’s learning statistical regularities—not one fixed script—it can combine patterns in new ways. If it has seen many examples of “explaining a concept” and many examples of “your app scenario,” it can often fuse them into a tailored response.
This is why an LLM can write a plausible onboarding email for a niche product, or adapt a generic API integration explanation to a specific stack. It’s not retrieving one stored paragraph; it’s generating a new sequence that matches patterns it learned.
Even if some training data included a specific fact (say, a pricing tier or an internal policy), you shouldn’t assume the model can reliably “look it up.” Training doesn’t work like indexing a knowledge base you can query later. It’s closer to compression: lots of examples get distilled into weights that influence future predictions.
That means the model can sound confident about details it’s guessing based on what usually appears in similar contexts.
Pattern learning is powerful for producing fluent, relevant text, but fluency is not the same as truth. The model may:
For app builders, the key takeaway is: an LLM’s answers usually come from learned patterns, not verified facts. If correctness matters, you’ll want to ground the output with your own data and checks (we’ll cover that in later sections).
When an LLM writes a reply, it’s not pulling a single “correct sentence” from a database. At each step, it predicts a range of possible next tokens (words or word-parts), each with a probability.
If the model always picked the single most likely next token, answers would be very consistent—but also repetitive and sometimes awkwardly rigid. Most systems instead sample from the probabilities, which introduces controlled randomness.
Two common settings shape how varied the outputs feel:
If you’re building an app, these knobs are less about “being creative” in an artistic sense and more about choosing between:
Because the model is optimizing for plausible text, it can produce statements that sound certain—even when the underlying claim is incorrect or missing key context. Confidence in tone is not evidence. This is why apps often need grounding (like retrieval) or verification steps for factual tasks.
Ask an LLM: “Write a JavaScript function that removes duplicates from an array.” You might get any of these, all valid:
// Option A: concise
const unique = (arr) => [...new Set(arr)];
// Option B: explicit
function unique(arr) {
return arr.filter((x, i) => arr.indexOf(x) === i);
}
Different sampling choices lead to different styles (concise vs explicit), different tradeoffs (speed, readability), and even different edge-case behavior—all without the model “changing its mind.” It’s just choosing among multiple high-probability continuations.
When people say an AI model “remembers” your conversation, what it really has is context: the text it can see right now—your latest message, any system instructions, and whatever portion of the earlier chat still fits.
The context window is a fixed limit on how much text the model can consider at once. Once the conversation gets long enough, older parts fall outside that window and effectively disappear from the model’s view.
That’s why you’ll sometimes see behavior like:
If you keep piling messages onto a thread, you’re competing for limited space. Important constraints get pushed out by recent back-and-forth. Without a summary, the model has to infer what matters from whatever remains visible—so it can sound confident while quietly missing key details.
A practical fix is to periodically summarize: restate the goal, decisions, and constraints in a compact block, then continue from there. In apps, this is often implemented as an automatic “conversation summary” that gets injected into the prompt.
Models tend to follow instructions that are close to the output they’re about to generate. So if you have must-follow rules (format, tone, edge cases), put them near the end of the prompt—right before “Now produce the answer.”
If you’re building an app, treat this like interface design: decide what must stay in context (requirements, user preferences, schema) and make sure it’s always included—either by trimming chat history or adding a tight summary. For more on structuring prompts, see /blog/prompting-as-interface-design.
LLMs are extremely good at producing text that sounds like the kind of answer you’d expect from a competent developer. But “sounds right” is not the same as “is right.” The model is predicting likely next tokens, not checking the output against your codebase, your dependencies, or the real world.
If the model suggests a fix, a refactor, or a new function, it’s still just text. It doesn’t actually run your app, import your packages, hit your API, or compile your project unless you explicitly connect it to a tool that can do those things (for example, a test runner, a linter, or a build step).
That’s the key contrast:
When AI gets things wrong, it often fails in predictable ways:
These errors can be hard to notice because the surrounding explanation is usually coherent.
Treat AI output like a fast draft from a teammate who didn’t run the project locally. Confidence should rise sharply after you:
If the tests don’t pass, assume the model’s answer is only a starting point—not a final fix.
A language model is great at proposing what might work—but by itself it’s still producing text. Tools are what let an AI-backed app turn those proposals into verified actions: run code, query a database, fetch documentation, or call an external API.
In app-building workflows, tools usually look like:
The important shift is that the model is no longer pretending it knows the result—it can check.
A useful mental model is:
This is how you reduce “guesswork.” If the linter reports unused imports, the model updates the code. If unit tests fail, it iterates until they pass (or it explains why it can’t).
eslint/ruff/prettier to confirm style and catch issues.Tools can be powerful—and dangerous. Follow least privilege:
Tools don’t make the model “smarter,” but they make your app’s AI more grounded—because it can verify, not just narrate.
A language model is great at writing, summarizing, and reasoning over text it can “see.” But it doesn’t automatically know your latest product changes, your company’s policies, or a specific customer’s account details. Retrieval-Augmented Generation (RAG) is a simple fix: first fetch the most relevant facts, then have the model write using those facts.
Think of RAG as “open-book AI.” Instead of asking the model to answer from memory, your app quickly pulls a handful of relevant passages (snippets) from trusted sources and adds them to the prompt. The model then generates an answer grounded in that provided material.
RAG is a great default whenever correctness depends on information outside the model:
If your app’s value depends on “the right answer for our business,” RAG is usually better than hoping the model guesses.
RAG is only as good as what it retrieves. If the search step returns outdated, irrelevant, or incomplete passages, the model may confidently produce a wrong answer—now “grounded” in the wrong source. In practice, improving retrieval quality (chunking, metadata, freshness, and ranking) often boosts accuracy more than tweaking prompts.
An “agent” is just an LLM running in a loop: it makes a plan, takes a step, looks at what happened, and decides what to do next. Instead of answering once, it iterates until it reaches a goal.
A useful mental model is:
Plan → Do → Check → Revise
This loop is what turns a single prompt into a small workflow. It’s also why agents can feel more “independent” than chat: the model isn’t only producing text, it’s choosing actions and sequencing them.
Agents need clear rules for when to stop. Common stopping conditions include:
Guardrails are the constraints that keep the loop safe and predictable: allowed tools, permitted data sources, approval steps (human-in-the-loop), and output formats.
Because an agent can always propose “one more step,” you must design for failure modes. Without budgets, timeouts, and step limits, an agent can spiral into repetitive actions (“try again with a slightly different query”) or rack up cost.
Practical defaults: cap iterations, log every action, require tool results to be validated, and fail gracefully with a partial answer plus what it tried. That’s often better product design than letting the agent loop forever.
If you’re building with a vibe-coding platform like Koder.ai, this “agent + tools” mental model is especially practical. You’re not only chatting for suggestions—you’re using a workflow where the assistant can help plan features, generate React/Go/PostgreSQL or Flutter components, and iterate with checkpoints (for example, using snapshots and rollback) so you can move fast without losing control of changes.
When you put an LLM behind an app feature, your prompt is no longer “just text.” It’s the interface contract between your product and the model: what the model is trying to do, what it’s allowed to use, and how it must respond so your code can reliably consume it.
A helpful mindset is to treat prompts like UI forms. Good forms reduce ambiguity, constrain choices, and make the next action obvious. Good prompts do the same.
Before you ship a prompt, make sure it clearly states:
Models follow patterns. One strong way to “teach” the pattern you want is to include a single example of a good input and a good output (especially if your task has edge cases).
Even one example can reduce back-and-forth and prevent the model from inventing a format your UI can’t display.
If another system will read the response, structure it. Ask for JSON, a table, or strict bullet rules.
You are a helpful assistant.
Task: {goal}
Inputs: {inputs}
Constraints:
- {constraints}
Output format (JSON):
{
"result": "string",
"confidence": "low|medium|high",
"warnings": ["string"],
"next_steps": ["string"]
}
This turns “prompting” into predictable interface design.
Add an explicit rule like: “If key requirements are missing, ask clarifying questions before answering.”
That single line can prevent confident-looking, wrong outputs—because the model is permitted (and expected) to pause and request the missing fields instead of guessing.
In practice, the most reliable prompts match the way your product builds and deploys. For example, if your platform supports planning first, then generating changes, then exporting source code or deploying, you can mirror that in the prompt contract (plan → produce diff/steps → confirm → apply). Koder.ai’s “planning mode” is a good example of how turning the process into explicit phases can reduce drift and help teams review changes before they ship.
Trust doesn’t come from a model “sounding confident.” It comes from treating AI output like any other dependency in your product: measured, monitored, and constrained.
Start with a small set of real tasks your app must do well. Then turn them into repeatable checks:
Instead of asking “Is it good?”, track “How often does it pass?” Useful metrics include:
When something goes wrong, you should be able to replay it. Log (with appropriate redaction):
This makes debugging practical and helps you answer “Did the model change, or did our data/tool change?”
A few defaults prevent common incidents:
It usually means the model can produce coherent, goal-directed text that looks like understanding and reasoning. In practice, an LLM is doing next-token prediction: it generates the most likely continuation given your prompt, instructions, and any provided context.
For app builders, the useful takeaway is that “thinking” is the output behavior you can shape and constrain—not an internal guarantee of truth.
A token is a chunk of text the model processes and generates (a whole word, part of a word, punctuation, or whitespace). Because models operate on tokens, not “sentences,” costs, limits, and truncation are all token-based.
Practically:
Because generation is probabilistic. At each step the model assigns probabilities to many possible next tokens, and most systems sample from that distribution rather than always choosing the single top option.
To make outputs more repeatable:
LLMs optimize for producing plausible text, not for verifying facts. They can sound certain because confident phrasing is a common pattern in training data, even when the underlying claim is a guess.
In product design, treat fluency as “good writing,” not “correctness,” and add checks (retrieval, tools, tests, approvals) when correctness matters.
The context window is the maximum amount of text the model can consider at once (system instructions, conversation history, retrieved snippets, etc.). When the thread gets too long, older information falls out of the window and the model can’t “see” it.
Mitigations:
Not automatically. By default the model isn’t browsing the web, reading your database, or executing code. It only has access to what you include in the prompt plus any tools you explicitly connect.
If your answer depends on internal or up-to-date facts, pass them in via retrieval (RAG) or a tool call rather than “asking harder.”
Use tools when you need verified results or real actions instead of plausible text. Common examples:
A good pattern is propose → check → adjust, where the model iterates based on tool outputs.
RAG (Retrieval-Augmented Generation) is “open-book AI”: your app retrieves relevant snippets from trusted sources (docs, tickets, policies) and includes them in the prompt so the model answers using those facts.
Use RAG when:
The main failure mode is poor retrieval—improving search, chunking, and freshness often beats prompt tweaks.
An agent is an LLM running a multi-step loop (plan, take an action, check results, revise) often using tools. It’s useful for workflows like “find info → draft → validate → send.”
To keep agents safe and predictable:
Treat prompts like an interface contract: define the goal, inputs, constraints, and output format so your app can reliably consume results.
Practical trust builders: