3 posts tagged with "prompt-engineering"

Most teams building on LLMs are overpaying by 60–90%. Not because they're using the wrong model or prompting inefficiently — but because they're reprocessing the same tokens on every single request. Prompt caching fixes this, and it takes about ten minutes to implement. Yet it remains one of the most underutilized optimizations in production LLM systems.

Here's what's happening: every time you send a request to an LLM API, the model runs attention over every token in your prompt. If your system prompt is 10,000 tokens and you're handling 1,000 requests per day, you're paying to process 10 million tokens daily just for the static part of your prompt — context that never changes. Prompt caching stores the intermediate computation (the key-value attention states) so subsequent requests can skip that work entirely.

Most teams reach for fine-tuning too early or too late. The ones who fine-tune too early burn weeks on a training pipeline before realizing a better system prompt would have solved the problem. The ones who wait too long run expensive 70B inferences on millions of repetitive tasks while accepting accuracy that a fine-tuned 7B model could have beaten—at a tenth of the cost.

The decision is not about which technique is "better." It's about matching the right tool to your specific constraints: data volume, latency budget, accuracy requirements, and how stable the task definition is. Here's how to think through it.

Most engineers learn prompt engineering backwards. They start with "be creative" and "think step by step," iterate on a demo until it works, then discover in production that the model is hallucinating 15% of the time and their JSON parser is throwing exceptions every few hours. The techniques that make a chatbot feel impressive are often not the ones that make a production system reliable.

After a year of shipping LLM features into real systems, here's what actually separates prompts that work from prompts that hold up under load.