debugging

When the right answer isn't even in the top-50, the generator can't rescue you. Low recall has a short list of usual suspects. Here is the order to check them so you fix the cause, not a symptom.

When a chain of LLM calls and tools yields a wrong final answer, 'the model was bad' is not a diagnosis. You need visibility into every step. Here is the tracing layer that turns a black box into something debuggable.

Six stages, six isolating experiments, six metrics. The candidates who score do not guess at knobs, they bisect the pipeline, and they instrument the rungs nobody else does: ranking, context assembly, generation.

The most frequent ML fundamentals question, and a subtle seniority check: anyone can repeat the definition, but can you break down the error and convert it into a concrete debugging plan?

'The model won't train' comes down to a short list of usual suspects, each with a distinctive loss-curve signature. The shape of the curve names the bug before you touch a single hyperparameter. Here is how to read it.

'The model got worse' can mean three very different things with three different fixes. Retrain a model that a pipeline bug actually broke and you just bake in garbage. Here is the order to triage it in.

When a metric looks off, lineage points you to the upstream table at fault. The signal is separating table from column lineage and knowing the three capture methods: SQL parsing, runtime hooks, and metadata APIs.

Same model, two answers: clean offline, ugly online. The cause is almost always a feature computed differently across the two paths. Here is the diff-based hunt that localizes it to a single column.

A 256-GPU job that freezes with no error at 3am is a special kind of pain. The causes come from a short, recurring list. Here is the systematic way to identify which one hit you.