Cost-Aware Prompting for Production LLM Apps
An LLM feature that works in demo blows the bill in production because nobody priced the prompt. This course covers the token-level engineering that keeps a feature profitable: where tokens actually go, prompt shaping for cost, caching the repeated context, and the model-routing decision that returns most of the saving.
Why this gap matters now
Demand signals we detected:
- token-bill threads up 200%
- production cost-vs-quality teams forming
- scarce content on token-level engineering tradeoffs
What you'll learn
- Where the tokens actually go — Most prompt cost is not the user's question; it is the context you re-send every turn — system prompt, retrieved passages, examples — billed in full on every call.
- Prompt shaping for cost — A verbose prompt is not just slow, it is expensive, and the model often performs no better for the extra words.
- Cache the repeated context — The context you send identically every call is the cheapest thing to cache.
- Route by difficulty, not by default — Routing everything to the strongest model is the most expensive default.
Who it's for
Engineers running LLM features against a token budget, 2–6 yrs.
Start learning
Read the full micro-course and earn a certificate of completion: Cost-Aware Prompting for Production LLM Apps.
