Precision Context Over Natural Language Only | Generated by AI
Question: Is it incorrect to call it the “LLM’s language”? Is the more accurate idea that prompts should include concrete context (like CSS selectors, HTML, or code) rather than just natural English?
Answer:
Yes — saying “LLM’s language” is not accurate. Your corrected understanding is better.
LLMs do not have a special language. They accept any text:
- English
- Chinese
- code
- HTML
- CSS selectors
- logs
- stack traces
- JSON
- mixed content
What actually matters is context precision, not “LLM language”.
Your idea can be expressed more precisely like this:
Core Idea
LLMs work best when the prompt contains concrete, machine-level context, not vague natural language.
Bad prompt (vague):
Click that button and extract the text
Good prompt (precise context):
Click this element:
<button class="btn-primary submit-order" data-testid="checkout-submit">
CSS selector:
button[data-testid="checkout-submit"]
Then extract text from:
<div id="order-confirmation">
The second works better because:
- removes ambiguity
- reduces hallucination
- gives exact DOM structure
- matches what the model sees in code
Why English-only prompts are weaker
Natural language:
click the blue button on the right
Problems:
- multiple blue buttons
- layout may change
- model must guess
But this is deterministic:
document.querySelector('#checkout > button.primary')
LLM doesn’t guess — it executes reasoning directly.
So the real principle
Not “LLM language”, but:
LLMs prefer structured context over descriptive language
Best prompt = mix of:
- natural language (intent)
- code (precision)
- selectors (location)
- logs (state)
- errors (feedback)
Example ideal prompt:
Goal: click checkout button
HTML:
<button id="checkout-btn" class="primary">
Selector:
#checkout-btn
Error:
Element not clickable at point (532, 410)
What's wrong?
This is extremely strong.
Why Copilot / Claude Code work better with copied code
Because they are context-driven, not language-driven.
When you paste:
- VSCode code
- DOM HTML
- CSS selectors
- stack trace
You reduce search space for the model.
Less guessing → better output.
Your corrected statement (most accurate)
Your improved understanding is correct:
- LLMs don’t need special language
- any text works
- but prompt + context must be self-consistent
- copying real code/HTML/selectors improves results
This is exactly right.
One-sentence summary
LLMs don’t require a special language — they perform best when prompts include precise, real context (code, selectors, logs) instead of vague natural language.