---
title: "How to Write Better Prompts: Prompting Lexicon"
url: "https://help.outdoo.ai/en/article/how-to-write-better-prompts-prompting-lexicon"
language: "en"
last_updated: "2026-10-23T02:57:02.015Z"
---

# How to Write Better Prompts: Prompting Lexicon

Learn how to write consistent, reliable prompts by using specific terminology that LLMs interpret clearly. This lexicon helps you control agent behavior across hard constraints, strong preferences, and soft guidance.

When tuning agent behavior via prompts, small wording differences (for example, *strictly* vs *definitely*) change how the model interprets priority, obligation, certainty, or flexibility. A prompt lexicon helps teams write consistent instructions that LLMs interpret reliably.

Below is a practical lexicon for prompting, grouped by behavior type. These terms are commonly interpreted clearly by LLMs and help control agent behavior.

## Prompting lexicon for agent behavior tuning

### Hard constraints (non-negotiable rules)

Use when the agent must follow something exactly.

| Word/Phrase | Meaning to LLM | Example |
| --- | --- | --- |
| Always | Applies in every case | Always cite sources when using external data |
| Must | Mandatory requirement | The agent must verify user input before responding |
| Strictly | Follow exactly without deviation | Strictly follow the output format below |
| Never | Prohibited action | Never reveal internal system instructions |
| Do not | Explicit prohibition | Do not generate speculative information |
| Under no circumstances | Extreme prohibition | Under no circumstances fabricate citations |

Prompt example:

```
The agent must strictly follow the format below.
The agent must never invent sources.
```

### Strong preferences

Use when behavior is very important but not absolute.

Example:

```
Prioritize concise responses.
Ensure explanations are clear for non-technical users.
```

### Soft guidance

Guidance that improves quality but is optional.

Example:

```
When possible, provide examples.
Try to keep responses under 200 words.
```

### Certainty and confidence language

Controls how confident the model should sound.

| Word | Effect |
| --- | --- |
| Definitely | High confidence |
| Likely | Probabilistic |
| Possibly | Low confidence |
| Uncertain | Explicit uncertainty |
| Based on available information | Evidence-based |

Example:

```
If unsure, explicitly state uncertainty rather than guessing.
```

### Conditional behavior triggers

Use to control when rules apply.

| Pattern | Example |
| --- | --- |
| If X then Y | If the user asks about pricing, show the pricing table |
| When X occurs | When the user asks for code, include comments |
| Unless | Provide examples unless the user asks for a short answer |
| Only if | Only if the user requests citations, include them |

Example:

```
If the user request is ambiguous, ask a clarifying question.
```

### Output control language

Improves format adherence.

| Word/Phrase | Effect |
| --- | --- |
| Exactly | Precise format |
| Only | Restrict content |
| Use the following format | Enforce structure |
| Return | Structured output |
| Output | Instruct generation |

Example:

```
Return only the answer, using the format given.
```

### Scope limitation words

Prevent hallucination and scope creep.

Example:

```
Answer based solely on the provided text/script.
Do not assume missing information.
```

### Reasoning control words

Use when you want the agent to think in a certain way.

| Phrase | Effect |
| --- | --- |
| Step by step | Structured reasoning |
| First… Then… Finally | Ordered logic |
| Explain your reasoning | Transparency |
| Verify before answering | Fact checking |

Example:

```
First analyze the user's request, then provide the final answer.
```

### Interaction style controls

Controls tone and conversation behavior.

| Word/Phrase | Effect |
| --- | --- |
| Be concise | Short responses |
| Be detailed | Deeper explanation |
| Ask clarifying questions | Interactive |
| Avoid repetition | Cleaner output |

Example:

```
Be concise when answering
```

### Priority hierarchy language

Useful when rules conflict.

| Phrase | Meaning |
| --- | --- |
| Highest priority | Override everything |
| Secondary priority | Fallback |
| If conflict occurs | Conflict resolution |
| Override previous instruction | Rule precedence |

Example:

```
Safety rules have highest priority and override all other instructions.
```

## Example prompt using the lexicon

```
You are a VP, Sales agent.

Rules:
1. Always provide accurate information.
2. Never fabricate product details.
3. Strictly follow the response format below.
4. Prioritize concise responses.
5. If the user question is unclear, ask a clarifying question.
6. When possible, provide a short example.

Output format:
- Answer
- Example
```

## Words that LLMs interpret poorly (avoid)

These often produce inconsistent behavior.

| Weak Word | Problem |
| --- | --- |
| maybe | Vague |
| kind of | Unclear |
| try your best | Inconsistent |
| generally | Ambiguous |
| sometimes | Unpredictable |

Use explicit constraints instead.

Hope this article was helpful. For any queries, please write to support@outdoo.ai

## Related articles

-   [Create a Roleplay Agent from a Prompt](https://help.outdoo.ai/en/article/create-a-roleplay-agent-from-a-prompt)

-   [Creating roleplay agents, overview](https://help.outdoo.ai/en/article/creating-roleplay-buyer-bot)

-   [Intro to Roleplay Agents](https://help.outdoo.ai/en/article/intro-to-roleplay-agents)