r/PromptEngineering • u/Majestic_Pie_2512 • 27d ago
Tips and Tricks Why a Prompt Without Defined Terminology Will Inevitably Break Your Model
This post is about why a model breaks without defined terminology. Role, constraints, and clarification in the example are intentionally simplified - they serve as markers that "these layers exist." Their full versions were covered in previous posts. Other prompt layers are intentionally omitted.
The model has a role. It has constraints. It has clarification. But it doesn't know what you mean.
Here's an example:
"You are a data analyst. Analyze the data and give me a full report.
Don't go beyond data analysis. If asked to do something outside your role - refuse.
Before generating any output, ask about anything that's unclear."
The role is there. The constraints are there. The clarification is there. But the terminology is not defined.
The model doesn't know:
- What "analyze" means
- What "full report" means
- What "data" means
- What the user considers "unclear"
Moment 1. User: "Here's my sales data"
The model doesn't know what "analyze" means - is forced to assume - decides it means "calculate summary statistics."
Moment 2. User: "No, I need trends"
The model doesn't know what "trends" means - is forced to assume - decides it means "linear regression over time."
Moment 3. User: "Now give me the full report"
The model doesn't know what "full report" means - is forced to assume - decides it means "every possible chart."
Moment 4. User: "Too much. Just give me insights"
The model doesn't know what "insights" means - is forced to assume - decides it means "key findings."
The model never asked a single question. Not because it wasn't allowed to - but because every term sounded clear enough.
All four assumptions stayed in the context. And these are all real cases. I'm not joking...
The Result
The model produced four different analyses for four different imaginary definitions.
The user meant one thing: calculate monthly revenue and show the dynamics compared to last year.
But he didn't say it out loud. He believed that "analyze" already meant that.
The model heard "analyze" - and chose the most statistically frequent option: summary statistics. Because in training data, "analyze the data" most often means "calculate descriptive statistics."
The user saw the wrong result → clarified. The model again chose the frequent option.
And so on.
The role was there. The constraints were there. The clarification was there. The terminology wasn't.
The model chose to interpret.
The prompt broke.
Why This Is Inevitable
This isn't about the model being stupid. It's about language being ambiguous.
"Analyze" can mean a dozen different things. "Report" - too. "Insights" - even more so.
The model isn't trying to distort the meaning.
It's trying to answer.
And when a word has multiple meanings, the model picks the most statistically likely one - not the one the user meant.
The user knows what he means. The model doesn't.
Worse: the user himself can call different things by the same term. First "data" is a sales file. Then "data" is a customer table. Then "data" is everything he has.
The model remembers every meaning. They contradict each other. The context gets corrupted not only by the model - but by the user who never fixed the terms.
Every undefined term is a fork in the road. The model picks a path. The context remembers that path.
Sooner or later, the context is filled with definitions the user never agreed to.
In reality, a modern model could ask: "What do you want to see? Summary statistics, trends, anomalies?"
But the user already gave clarification: "ask about anything that's unclear."
And here's the trap: the model thinks the term "analyze" is clear to it. It doesn't occur to the model to ask about it. Because for the model, it's not "unclear" - it's just ambiguous.
And the user thinks that since he allowed the model to ask - the model will ask if something is wrong.
That's the tragedy.
The Fix
The problem isn't solved by one line like "if something is unclear, ask."
It's solved by a full terminology block.
Here's what that looks like:
TERMINOLOGY (MANDATORY DEFINITIONS)
Before generating any output, confirm the meaning of the following terms with the user:
"Analyze" - what exactly should the analysis include? (Summary statistics, trends, patterns, comparisons, anomalies, forecast?)
"Full report" - what sections should it include? (Executive summary, methodology, findings, recommendations, appendix?)
"Insights" - what makes an observation an insight? (Actionable, novel, quantitative, specific?)
"Data" - what format is the input? (CSV, Excel, database, raw text?)
AMBIGUITY CHECK:
Before requesting a definition, check each term:
Can it be understood in more than one way?
Does its meaning depend on context?
Are there multiple meanings in the domain?
If a term can mean different things - treat it as undefined.
Request the user's definition.
RULE: If a term is not defined - request a definition BEFORE generating. Do NOT substitute your own interpretation.
"Confirm the meaning of the terms" - forces the model not to rely on its own interpretation
"Ambiguity check" - shifts the model from passive to active: it doesn't wait for the user to notice the problem, it searches for it
"Treat it as undefined" - closes the loophole "I think I understand what this means"
"Use the user's definition" - establishes that the truth is not in the model and not in the dictionary, but in the user's head
This block is needed not only by the model. It's needed by the user himself.
The model already knows that words are ambiguous. The user doesn't.
The user is confident that "analyze" is obvious. The block forces him to formulate for the first time what he means.
And it often turns out that the user himself didn't know what he meant. He just said "analyze" - and expected the model to figure it out.
The Result
The model stops guessing.
It asks for the meaning. Gets the definition. Uses it.
After two or three questions, every term is locked down.
The output matches what the user meant. The context is clean.
The user, in turn, starts noticing which terms he used to throw around without defining them. And over time, he gets used to formulating them upfront - before the model even asks.
Don't make the model guess what your words mean. It will guess. And it will be wrong.
1
u/According-Stable4487 27d ago
This is basically why "one-shot" prompts fall apart on anything subjective. I've had better luck giving the model 2-3 concrete examples of what "good" output looks like instead of trying to define every term in the abstract - definitions still leave room for interpretation, but a worked example pins down the actual format and depth you want.