SmartRouter — One API for All AI Models

After processing over 10 million LLM requests in production, here are the patterns that actually work — and the ones that do not.

Lesson 1: Structured Output Is Non-Negotiable

The single biggest cause of production failures: the model returns valid-looking text that does not parse. Always use structured output modes:

• OpenAI: response_format: { type: "json_object" } or function calling
• Anthropic: Use XML tags in the prompt and parse with a schema validator
• All providers: Append "Respond with valid JSON only. No markdown, no explanation."

Lesson 2: Temperature Is Overrated

Most applications use temperature=0.7 by default. For production:

• temperature=0 for: classification, extraction, data transformation, code generation
• temperature=0.3-0.5 for: Q&A, summarization, email drafting
• temperature=0.7-1.0 for: creative writing, brainstorming, dialogue

Lesson 3: Token Counting Prevents Cost Explosions

Always enforce max_tokens. Always. A prompt that accidentally includes a 50K token context can cost $2-10 per request if unconstrained. Set max_tokens based on your expected output length.

Lesson 4: System Prompts Matter More Than You Think

A well-crafted system prompt:

• Reduces prompt length by 30-50% (instructions do not need to be repeated per message)
• Improves consistency across providers
• Makes prompt version control cleaner (system prompt changes separately from user messages)

Lesson 5: Multi-Provider Prompt Compatibility

A prompt that works on GPT-4o might fail on Claude or Gemini. Key differences:

• Claude prefers XML-style formatting in prompts
• Gemini sometimes ignores system prompts unless placed at the start
• DeepSeek handles very long contexts differently

SmartRouter helps by testing your prompts across providers and reporting compatibility issues: https://smartrouter.online

Try SmartRouter free — 50+ models, one OpenAI-compatible API. Auto-routing, failover, cost optimization.

Get Started