debugging

When the right answer isn't even in the top-50, the generator can't save 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 produces a wrong final answer, 'the model was bad' is not a diagnosis. You need to see every step. Here is the tracing layer that turns a black box into something debuggable.

A 0.87 cosine score on a chunk that answers nothing is not a bug in your vector database. It is the embedding model telling you exactly what it was trained to tell you. Here is how to read that signal instead of thresholding it away.

The most common ML fundamentals question, and a quiet seniority test: anyone recites the definition, but can you decompose the error and turn it into a concrete debugging plan?

'The model won't train' has 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' has three very different causes and three different fixes. Retraining a model that's actually broken by a pipeline bug just bakes in garbage. Here is the triage order.

When a metric looks wrong, lineage tells you which upstream table to blame. The signal is distinguishing table vs column lineage and knowing the three ways to capture it: SQL parsing, runtime hooks, and metadata APIs.

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

A 256-GPU job that wedges with no error at 3am is a special kind of pain. The causes are a short, recurring list. Here is the systematic way to find which one bit you.