LLM-Assisted Training Report Generation in a Distributed MLOps Pipeline: Architecture, Deterministic Fallback, and Empirical Evaluation

Every YOLO training run on the NeuralForgeAI cluster leaves behind loss curves, confusion matrices, and a growing set of forensic JSON reports from post-training evaluators. Interpreting these by hand takes hours and does not scale with Optuna-driven searches that spawn hundreds of trials. We document LlmAnalyzer, a WPipe step that runs as the final stage of a 15-step post-training pipeline and converts results.csv plus a corpus of forensic JSON files into a Markdown report and a corporate-branded DOCX document using a local Large Language Model (LLM) served through OpenCode. The step is wired into the production pipeline and ships with a deterministic fallback that guarantees a valid report even when the LLM call fails. We measure that fallback end-to-end: median 0.03 ms over 120 runs on six real results.csv artifacts, and recovery from a simulated LLM crash in 0.12 ms. The LLM path completed in 8.0--13.8 s (median 12.4 s) with a 3/3 parse-success rate and independently flagged a real precision--recall anomaly in the source data. Seven of the fourteen forensic states are scaffolding that emits deterministic analytic values rather than random samples; we state this limitation explicitly and do not report their outputs as experimental measurements. The pipeline is released under a Dual License (PolyForm / AGPLv3) through the wyoloservice2_production repository.