i think railway is good and cheap here. but in my case i dont need database at monumoney.in so i use vercel only.

Strong framing Suny Choudhary! Every failure mode listed traces back to the same root: nobody's measuring input quality before it hits the model or the next step in the chain. The fix everyone's describing (structured handoffs, context discipline, validation at boundaries) is right, but it's still being done by vibes. "Is this context good enough?" "Is this handoff clean?" Answered by feel. We recently scored 500 production prompts on 8 dimensions (grounded in PEEM, RAGAS, MT-Bench, G-Eval, ROUGE). Zero passed. Average 13.3/80, which means models are running at ~13% of capability on the inputs people actually ship. Weakest dimension across the board: Examples (1.01/10). The cascading failure point that Archit raised is the hardest case. Single-step prompts fail loud. Chained pipelines fail silent, and each hop multiplies the cost before anyone notices. Measuring the input at every boundary is the only way I've found to catch drift before it compounds. This stuff is measurable. We just built the measurement layer: PQS — Prompt Quality Score. Happy to share the data if anyone wants to dig in. 🔗 pqs.onchainintel.net

I never looked at AI this way, but what you have explained makes lot of sense !! thanks for sharing the skills

Feels like we didn’t remove friction, we just moved it to “verification.” If users have to double-check every output, it’s not solving UX. For me, if it breaks predictability, it’s not worth shipping.

Awesome read! Really appreciate how you broke down try, catch, and finally blocks with practical code snippets. It’s a great reminder to always handle failures gracefully instead of letting the app hang. Great work!

Django's model introspection is super underused for this exact kind of tooling — every ops team ends up hand-maintaining an ERD that drifts the moment someone adds a migration. One nice extension: combine _meta.get_fields() with Django's app_label grouping, then spit out Mermaid ER syntax so it renders natively in Markdown docs and Hashnode articles. For clients on large legacy schemas we also inject foreign_key reverse relations, which the default schema view usually misses. Did you consider exposing it as a management command so CI can snapshot the schema on each migration?