do I need to worry about token limits in production?

Yes — you should treat token limits as a real production concern.

Why it matters

• Requests can fail if the prompt + conversation history + generated output exceed the model’s context window.
• Costs rise with longer prompts and outputs.
• Latency increases as token counts grow.
• Quality can degrade when the model has to juggle too much context.

What to watch

• Context window limit: max tokens the model can handle in one request.
• Output limit: max tokens the model can generate in one response.
• Hidden overhead: system prompts, tool calls, JSON formatting, and chat history all consume tokens too.

Production best practices

• Track token usage for every request.
• Trim or summarize conversation history in chat apps.
• Chunk long documents instead of sending everything at once.
• Set sensible max output tokens to prevent runaway responses.
• Use retrieval (RAG) to pull only relevant content.
• Add fallback handling for overflow cases.

Practical rule

If your app:

• stores chat history,
• processes long documents,
• uses tool/function calling,
• or generates structured outputs,

then yes, token limits can absolutely show up in production.

If you want, I can help you estimate token usage for your specific app or model.