How to Properly Benchmark a Unified LLM API for Invoice Data Extraction
Developers building Node.js invoice-extraction backends that route requests through a unified LLM API — supporting OpenAI, Claude, or Gemini via a single credential — must benchmark the full pipeline against both field-level quality thresholds and latency budgets before relying on it in production. A curated acceptance corpus of around 20 labeled documents, paired with reviewed expected outputs, can expose critical extraction failures that aggregate scores alone would obscure. Exact fields like invoice numbers and currency codes require normalized value comparisons, while latency should be measured at a percentile level rather than just an average to avoid masking slow outliers that could bottleneck support queues. Benchmark runs must use identical inputs, schema instructions, and retry policies across all candidate paths, and evaluators must distinguish between syntactically valid JSON and genuinely correct field extraction. The key takeaway is that a unified gateway's convenience — one API key and a clean interface — does not substitute for rigorous, representative testing against the actual documents a team processes.
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