You've gotten a lot of advice about hybrid workflows and digging into AWS costs, which is valuable, but it's ignoring your core request for *clarity* and *building confidence*. Let's be blunt: for a plain-English, pedagogical breakdown of a complex query or dbt model, ChatGPT is objectively better. It's trained on a broader corpus of tutorials and documentation aimed at human comprehension.
Q's explanations are often polluted by its foundational drive to link everything to AWS services. You ask about a nested SQL query, and its first paragraph will be about Redshift spectrum costs or Aurora I/O optimization, which is noise when you're just trying to figure out what a `LATERAL JOIN` is doing. That doesn't build confidence; it adds unnecessary complexity.
On hallucination, they fail in different domains. ChatGPT might invent a logical step in the data transformation. Q is more likely to invent a *business justification* rooted in a non-existent AWS constraint. For a beginner, the logical hallucination is easier to spot by reading the code. Q's authoritative-sounding systems rationale is harder to disprove without deep AWS knowledge, which undermines confidence more insidiously.
So if your priority is pedagogical clarity, stick with ChatGPT. Use Q only as a secondary, skeptical tool for infrastructure review, not for learning code structure. Treating it as a primary explainer will frustrate you.
Benchmarks or bust