Prompt Optimizer
Improve prompt structure, clarity, and reliability offline using static rules analysis and professional templates.
Prompt Workspace
Best Practices Analysis
See the structural validation of your current prompt below:
Role/PersonaWarning
No explicit role/persona detected. Adding 'You are a [expert]' helps contextualize the model's responses.
XML Structural TagsWarning
No structural XML tags found. Tags help the model separate data parameters from instructions.
Variables / PlaceholdersTip
No variable placeholders detected. If reusing this prompt, consider parametrizing variable inputs.
Minimum LengthWarning
The prompt is quite short. Detailed prompts tend to yield significantly better model outputs.
Prompt Engineering: Best Practices
A well-structured instruction allows the model to understand its role, filter context correctly, and strictly follow constraints.
1. Persona Assignment
Always start by specifying the model's specialty and tone. Saying "Act as a senior financial analyst" directs the vocabulary and depth of the response.
2. Structural Delimiters
Use tags like <context> or <instructions> to encapsulate input data. This prevents the model from mixing the content to analyze with execution instructions.
3. Few-Shot Learning
Models are excellent pattern imitators. Showing real examples of "Input → Output" before the final question is the most effective technique to force specific formatting.
4. Clear Constraints
Explicitly state what the model **must not** do, for example: "Do not assume data not provided" or "Do not format the response with markdown tags".
Frequently Asked Questions (FAQ)
How does the static validator (Linter) work?
It analyzes common linguistic and syntactic patterns in prompt engineering (such as the presence of personas, the correct use of structural tags, and variable formatting). It runs 100% offline via fast regular expressions.
Why use XML delimiters instead of Markdown headings?
Modern frontier models (like Gemini, GPT-4, and Claude) have been extensively trained on code and tags. Delimiters in the format <tag>...</tag> have a clear start and end demarcation, which minimizes "instruction injection" when the user's context contains command keywords.
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