GPT-4 and the Reasoning Gap: What Changes When the Model Can Think About Dependencies

GPT-4 dropped last month and I've been running it alongside GPT-3.5 on the same data engineering tasks. The performance difference on syntax is negligible — both models generate correct PySpark syntax, both handle standard DataFrame operations, both know the Airflow operator API. On that dimension, the upgrade is barely visible.

The difference that matters is reasoning about dependencies, consequences, and trade-offs. And on that dimension, GPT-4 is a step change.

A Concrete Comparison

I gave both models the same prompt: a Spark job description with a subtle structural problem, and asked them to review it for correctness.

The job reads a daily events partition, joins against a user dimension, filters for active users, and writes the result. The subtle problem: the filter for active users happens after the join, but the user dimension only contains current active users — not historical records. Users who deactivated between the event date and the join date would be dropped from historical records as a side effect.

GPT-3.5 reviewed the code and flagged the join type (LEFT vs INNER), suggested an index on the join key, and noted that the filter should check for null user_ids from the left join. It didn't catch the historical accuracy problem.

GPT-4 caught the historical accuracy problem directly. Its response included: "The filter on is_active = true applied post-join will silently exclude historical events from users who were active at event time but have since been deactivated. If you need point-in-time accuracy, the dimension join needs to use a snapshot of the user table as of the event date, not the current state."

That's the reasoning gap. GPT-3.5 reviewed the code. GPT-4 reasoned about the temporal semantics of the data.

Where the Improvement Shows Up

The pattern repeats across the tasks I've tested. GPT-4 is materially better at:

• Dependency chains. Identifying that a transformation's correctness depends on an upstream assumption that isn't guaranteed by the code.
• Partition semantics. Understanding that a query filtered by event_date will scan all partitions if the predicate is applied after a join that expands the date range.
• Schema evolution consequences. Flagging that adding a NOT NULL column to a table that's written by multiple jobs will break the other writers before it breaks the readers.
• Null propagation. Tracing how a nullable field flows through a series of transformations and identifying where a null produces a wrong result rather than an error.

What Hasn't Changed

GPT-4 still doesn't know your data. It doesn't know that your user_id nulls mean "anonymous" and not "bad data." It doesn't know your partition conventions or your audit table pattern. It reasons well about what you tell it; it doesn't know what you haven't told it.

The confidence calibration problem also persists. GPT-4 is wrong less often, but when it's wrong, it's wrong with the same tone as when it's right. The improved reasoning makes it easier to spot the errors when you read carefully — the logic is now visible enough to check — but you still have to read carefully.

The Practical Upgrade

For rubber duck sessions and code review, GPT-4 is a meaningful upgrade. I'm catching classes of pipeline design errors in the conversation that previously only surfaced in testing or in production. That's the right direction. Use it on anything where reasoning about data semantics matters — which in data engineering is most things. As always, I'm here to help.