Start with one narrow, high-value use case
Don't sprinkle AI everywhere. Find the single workflow where it clearly earns its cost — answering repetitive support questions, drafting something users would otherwise do by hand. A focused feature is cheaper, easier to evaluate, and easier to trust. The 30% support reduction I shipped came from one well-chosen use case, not a dozen AI buttons.
Ground answers in your data (RAG)
For anything that must reflect your product's reality, retrieve relevant context and generate from it, rather than hoping the model knows. RAG keeps answers current and citable, and capping the retrieved context is also a direct cost control — fewer tokens per call.
Right-size the model
The biggest model is rarely the right default. Pick the smallest model that passes your evals for the task; reserve larger models for the genuinely hard calls. Model choice is the single largest lever on your bill.
Cache, stream, and budget
- Cache: identical or near-identical prompts shouldn't pay twice.
- Stream responses so the feature feels fast without a bigger model.
- Budget tokens per feature and alert on anomalies — a runaway prompt loop should page you, not surprise you at invoice time.
Cost control isn't an optimization you do later — it's a design constraint you build in from the first prompt. Decide the token budget per feature before you ship it.
Define evals before launch
You can't improve — or safely cost-optimize — what you don't measure. Write a small evaluation set for the feature, track quality over time, and use it to justify dropping to a cheaper model. Treat LLM output as untrusted: validate it, constrain it with schemas/tools, and defend against prompt injection.
Key takeaways
- One narrow, high-value use case beats AI sprinkled everywhere.
- Ground with retrieval; cap context for accuracy and cost.
- Right-size the model — the largest is rarely the right default.
- Cache, stream, and budget tokens; build cost control in from day one.
- Evals before launch; treat model output as untrusted.