How do you navigate the latency, cost, quality triangle when choosing an eval approach?

Evaluating an LLM product is about measuring quality without spending too much money or time. You have three tools, and they sit at different corners of a **latency, cost, quality triangle**. Automated metrics are the cheapest and fastest. They run in milliseconds and cost almost nothing per check. Things like format validation, length limits, regex rules, or scores like ROUGE fit here. The catch is low fidelity, they cannot judge whether an answer is actually helpful. LLM-as-judge sits in the middle. You ask a strong model to score outputs on quality, faithfulness, or relevance. It takes seconds and costs cents per call, and it catches nuance that rules cannot. But it has biases and is not free. Human evaluation is the gold standard, and the most expensive and slowest. A person reads outputs and judges them, which costs dollars per item and takes days. The key idea is to not pick one. Layer them. Run cheap automated checks on everything, run the judge on a small sample, and use occasional human review to confirm the cheaper layers are still tracking real quality.

The three eval modes occupy different corners of the **latency, cost, quality triangle**. Automated metrics are fast, cheap, and low fidelity. LLM-as-judge is medium on all three. Human evaluation is slow, expensive, and the highest fidelity. No single mode wins. The right design is a cascade, where each tier is cheaper than the next and the cheap tiers absorb the volume. The recommended layering has three stages. First, automated checks act as a continuous gate on every output. They block obvious failures cheaply: bad format, length violations, missing citations, banned content. Second, an LLM judge scores a sampled slice of traffic, often 1 to 5 percent daily, for nuanced signals like helpfulness and faithfulness. Third, human review runs on a tiny holdout, quarterly or when scores shift. The escalation principle ties it together. Cheap layers run at scale and filter the easy cases. Expensive review is reserved for the hard or high-stakes cases the cheap layers flag. Human eval plays a special role here: calibration. You compare judge scores against human labels on the holdout. Then you recalibrate or reprompt the judge when agreement drops. That keeps the cheap layers honest over time.