Editing Prompt Engineering (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a robust prompt engineering workflow:

'''1. Prompt development methodology'''
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Define task precisely: what input → what output?
    ↓
Write initial zero-shot prompt
    ↓
Evaluate on 20+ diverse examples
    ↓
Identify failure modes → add constraints, examples, CoT
    ↓
[Iterate: specify → test → analyze → refine]
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Adversarial testing: injection, edge cases
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Regression suite: 100+ examples with expected outputs
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A/B test in production
    ↓
Monitor and maintain
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'''2. System prompt template for a production assistant'''
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# Role
You are [NAME], a [DOMAIN] expert assistant for [COMPANY/APP].

# Capabilities
You can: [list what the model SHOULD do]

# Constraints
You must NOT: [list prohibited behaviors]
If asked about [OFF-TOPIC], respond: "[standard deflection]"

# Output Format
Always respond in [FORMAT]. Structure your response as:
1. [FIELD 1]
2. [FIELD 2]

# Tone
[Formal/Casual/Technical]. Avoid [specific patterns to avoid].

# Grounding
Base your responses only on [CONTEXT SOURCES]. If unsure, say "I don't know."
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'''3. Meta-prompting for prompt generation'''
* Ask a capable model to generate prompt variants for your task
* Use the model to critique and improve your draft prompt
* Use "prompt optimizers" (DSPy, PromptBreeder) that automatically search the prompt space

'''4. Prompt management infrastructure'''
* Store prompts in version control (Git) alongside code
* Use prompt management platforms (LangSmith, Promptfoo, Langfuse)
* Keep prompts in configuration files, not hardcoded in source
* Implement prompt A/B testing with statistical significance tracking

[[Category:Artificial Intelligence]]
[[Category:Large Language Models]]
[[Category:Prompt Engineering]]
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