A Timestamp in Your System Prompt Is Multiplying Your LLM Bill
Article summary
Quick briefing — cleaned from the original RSS feed
The problem Open the usage dashboard of any team running LLM agents and you'll see the same shape: input tokens dwarfing output tokens, often 30-50x. That ratio isn't the model being verbose. It's your architecture re-buying the same tokens over and over. LLM APIs are stateless. Every call starts cold and processes your entire prompt from token zero. An agent loop resends the whole conversation every turn — system prompt, tool schemas, every prior tool call and result — plus one new message.…
1Key Takeaways
- The problem Open the usage dashboard of any team running LLM agents and you'll see the same shape: input tokens dwarfing output tokens, often 30-50x.
- That ratio isn't the model being verbose.
- It's your architecture re-buying the same tokens over and over.
- Every call starts cold and processes your entire prompt from token zero.
2AIWedia Score
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3Why it matters
Prompt and agent patterns spread fast; staying current saves time and token cost. DEV — Prompt Engineering reports that the problem Open the usage dashboard of any team running LLM agents and you'll see the same shape: input tokens dwarfing output tokens, often 30-50x.
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