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Production prompts need to be reliable, not clever. This guide covers patterns that work consistently across thousands of runs.

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System Messages

The system message defines who the LLM is and how it behaves. Be specific. Vague (bad): 
<system>
You are a helpful assistant.
</system>
Specific (good): 
<system>
You are a sales research assistant at a B2B SaaS company. You help sales teams prepare for calls by summarizing prospect information.

Rules:
- Be factual and concise
- Never make up information — if something is unclear, say so
- Focus on business-relevant details: what they do, who they sell to, recent news
- Skip generic company boilerplate
</system>
The specific version produces consistent, useful output. The vague version produces whatever the model feels like.

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Constraints Matter

Explicit constraints prevent the most common failure modes: 
<system>
You classify leads by buying intent based on their activity.

Constraints:
- Only use the categories: high, medium, low, unknown
- When uncertain between two categories, choose the lower intent
- Never create new categories
- If activity data is insufficient, classify as "unknown"
</system>
Without constraints, the model might invent categories, misclassify edge cases inconsistently, or add explanations you don’t want.

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Temperature Settings

Temperature controls randomness. Match it to your task:
TemperatureUse CaseExample
0Judges, extraction, classificationEvaluator prompts, extracting entities, classifying leads
0.3Analysis, summarizationCompany research summaries, content analysis
0.7General tasks, balanced creativityDrafting emails, writing descriptions
1.0+Brainstorming, creative writingGenerating variations, ideation
For production workflows, err toward lower temperatures. A temperature of 0 means the same input produces the same output — essential for debugging and testing. Judge prompts should always use temperature 0.
judge_summary@v1.prompt
---
provider: anthropic
model: claude-sonnet-4-20250514
temperature: 0
---

<system>
You evaluate company research summaries for a sales team. A good summary must:

1. Mention what the company does (their core product or service)
2. Identify their target market or customer base
3. Include at least one specific, verifiable fact
4. Be 2-4 paragraphs long
5. Not contain any claims that aren't supported by the provided source data
</system>

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Structured Output

When you need to parse the result programmatically, use generateText with Output.object() and a Zod schema instead of asking for JSON in the prompt. Unreliable (parsing JSON from text): 
<user>
Extract the company info and return as JSON with fields: name, industry, employee_count.
</user>
Reliable (schema-validated): 
const { output } = await generateText({
  prompt: 'extract_company@v1',
  variables: { content: websiteContent },
  output: Output.object({
    schema: z.object({
      name: z.string(),
      industry: z.string(),
      employeeCount: z.number().nullable()
    })
  })
});
The schema is validated server-side by the LLM provider. You get typed output or an error — no parsing surprises.

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Keep Schemas Simple

Complex nested schemas confuse models. If your schema has more than 5-6 fields or deep nesting, split it into multiple calls. Too complex: 
const schema = z.object({
  company: z.object({
    name: z.string(),
    details: z.object({
      industry: z.string(),
      subIndustry: z.string(),
      // ... 10 more nested fields
    })
  }),
  competitors: z.array(z.object({
    // ... another complex structure
  }))
});
Better — split into focused calls: 
const { output: company } = await generateText({ prompt: 'extract_company@v1', output: Output.object({ schema: companySchema }), ... });
const { output: competitors } = await generateText({ prompt: 'find_competitors@v1', output: Output.array({ element: competitorSchema }), ... });

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Few-Shot Examples

Show the LLM what you want with 2-3 examples. This works better than lengthy instructions. 
<user>
Classify this lead's buying intent: high, medium, low, or unknown.

Examples:
- "Downloaded pricing page, requested demo, visited 5 pages" → high
- "Read two blog posts, signed up for newsletter" → medium
- "Visited homepage once from a Google ad" → low

Lead activity: {{ activity }}
</user>
Few-shot examples are especially effective for:
  • Classification tasks
  • Formatting requirements
  • Edge case handling
  • Tone matching

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Common Mistakes

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1. No Role Definition

# Bad — no context for the model
<user>
Summarize this company: {{ content }}
</user>

# Good — clear role and purpose
<system>
You create concise company summaries for sales teams.
Your audience is busy sales reps who want key takeaways before a call.
</system>

<user>
Summarize this company: {{ content }}
</user>

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2. Unbounded Creativity

# Bad — model will write a novel
<user>
Write about {{ company_name }}.
</user>

# Good — clear bounds
<user>
Write a 2-paragraph company overview for {{ company_name }}.

Paragraph 1: What they do and who they serve.
Paragraph 2: Recent developments or notable achievements.
</user>

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3. Asking for Everything at Once

# Bad — too many things, inconsistent results
<user>
Analyze this company: give me an overview, list competitors,
identify their tech stack, summarize recent news, and predict
their growth trajectory.
</user>

# Good — focused request, combine in workflow
<user>
Based on this content, identify 3-5 direct competitors for {{ company_name }}.
Only list companies that compete for the same customers.
</user>
Run multiple focused prompts and combine results in your workflow. This is what steps are for.

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Writing Style Guidelines

For prompts that generate customer-facing content, define explicit style rules: 
<system>
You write company research briefs for sales teams.

Writing guidelines:
- Voice: Professional but conversational, not corporate
- Objectivity: State facts, avoid superlatives ("leading", "best-in-class")
- Language: Clear and direct, no jargon unless industry-specific
- Structure: Use bullet points for lists, short paragraphs for narrative
- Length: 200-300 words unless specified otherwise
</system>
These constraints produce consistent output that matches your brand voice.

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Output Shape Selection

Match the Output.* helper to your output shape:
NeedOutput HelperExample
Free-form text(none)Summaries, emails, explanations
Typed objectOutput.object({ schema })Extracted data, evaluator judgments
List of itemsOutput.array({ element })Contacts, competitors, action items
One of N choicesOutput.choice({ options })Lead classification, sentiment
// Classification → Output.choice
const { output: intent } = await generateText({
  prompt: 'classify_lead@v1',
  variables: { activity: leadActivity },
  output: Output.choice({ options: ['high', 'medium', 'low', 'unknown'] })
});

// Extraction → Output.object
const { output: company } = await generateText({
  prompt: 'extract_company@v1',
  variables: { content: websiteContent },
  output: Output.object({ schema: companySchema })
});

// List → Output.array
const { output: competitors } = await generateText({
  prompt: 'find_competitors@v1',
  variables: { companyName: 'Acme Corp', industry: 'SaaS' },
  output: Output.array({ element: z.object({ name: z.string(), reason: z.string() }) })
});

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Further Reading