How teams scaled LLMs: 2 real case studies
2 of the 94 engineering write-ups in our database mention LLMs in their stack. They come from 2 companies (Meta and Netflix) and were published in 2026. 2 of the 2 state concrete scale numbers, for example "hundreds of millions of lines of code" (Meta). Each card below links to our short summary and the original post.
Catching Bugs Before They Land: JiTTesting's Human-on-the-Loop Approach at Full Scale
Meta's Catching Just-in-Time Tests (JiTTests) are now production infrastructure. Overnight, on spare capacity, the system picks risky diffs from the previous day and generates tests designed to fail: a "dodgy diff" workflow treats each change as a mutant, while an intent-aware workflow infers what the diff is meant to do, lists the risks, and builds mutants from them. An ensemble of LLM judges plus a deterministic rule-based assessor (RubFake) ranks the failing "weak catches", and engineers are only contacted for the strongest ones. 589 engineers have engaged, reporting 60% true positives, and in the first half of 2026 it helped catch 74 serious privacy bugs before landing, cutting privacy SEVs by 20%.
Ontology-Driven Observability: Building an End-to-End Knowledge Graph at Netflix Scale
Netflix found a typical regression investigation took about four hours, mostly spent reconstructing relationships between systems like Atlas, Radar, Spinnaker, PagerDuty, incident.io and Slack that each named the same thing differently. It built an Observability Ontology on the W3C stack (RDF, OWL, SHACL, SPARQL), stored as a knowledge graph in QuipuDB, its homegrown distributed quad store built on the Rust key-value store redb. Enrichers, some LLM-based, turn noisy inputs into typed triples, every application is validated against a catalog of about 49,000 entries, and dependency edges are capped. A correlation engine scores chains across 13 signal types within a ten-minute window, discarding anything under 0.6 confidence, and an agentic AutoRCA system reads the graph to hand engineers ranked, evidence-backed hypotheses.