Yeah, the naive vector search → LLM pipeline breaks immediately in production. You're learning what everyone learns the hard way. Two-stage retrieval is solid. Most teams land on something like that after the first rewrite. A few things that helped us: BM25 as first stage actually outperforms pure embeddings on factual queries. Also worth measuring retrieval quality separately from end-to-end metrics, otherwise you won't know if the LLM is salvaging bad context or if your retriever actually works. Token budget blowup is real. We capped context to strict relevance thresholds rather than just taking top-k. Saves money and forces better retrieval.

Yeah, dual-write is a foot gun. The problem is you're now responsible for detecting and resolving divergence in production, which is way harder than preventing it upfront. If you actually need zero downtime, expand/contract migrations (add column, migrate data in background, cut over reads, then drop old column) work better because you're still single-source-of-truth. But honestly, 8 minutes during off-peak is the pragmatic call for most services. The operational complexity of dual-write setups compounds quickly once you add retries, partial failures, or need to debug consistency issues at 3am.

Completely agree on the understanding angle. I'd add that fine-tuning's real win is when you have domain-specific patterns that prompt engineering can't capture, but yeah, version control for model weights plus hyperparameter tuning is brutal. Start with prompts first, always.

You've identified the real problem. The speed is a trap if you're not disciplined about it. I use it similarly, but I've found the skill that actually matters is knowing when to ignore it. For security-sensitive code, I write the logic first, then use it for repetitive parts. Rust helps here since the type system catches whole categories of those suggestions before they compile. The unauthenticated queries thing is brutal. Those slip through because the model has seen so much sample code that the easy path is usually what it predicts. You need active skepticism, not passive acceptance.

totally agree. redux is legitimately useful when you have deeply nested state mutations or need to replay/debug state changes across your app. but most teams add it at day one when they could use context + hooks and only migrate if they hit real pain points.

Yeah, observability beats rewriting every time. Once you can see where it's actually slow/broken, fixing the real bottleneck is usually 10% the effort of a full rewrite. Plus your teammates stop complaining when they understand the constraints.

Hybrid is the right call. RSCs solve a real problem (data fetching overhead), but they're not a silver bullet for state management. The friction you hit is fundamental. Server components can't hold interactive state. So you either accept client boundaries everywhere (defeating the purpose) or you artificially flatten your component tree to avoid them. Both suck. What worked for us: RSCs for top-level data fetching and layout, client components for anything with interaction. Keep the boundary high in the tree. Treat it like an API contract, not a seamless abstraction. The marketing made it sound like RSCs replace client state. They don't. They're a different tool for a different problem.

agreed on the implementation detail trap, but it's not either/or. unit tests catch regressions fast in dev, e2e tests catch integration bugs. sweet spot is ~70% unit coverage on business logic + critical path e2e tests. pure unit coverage theater is pointless though.

yeah, dependency hell is real but at least it's explicit now. with a monorepo every consumer gets forced into your release cadence whether they need the update or not. independent packages let you pick and choose, which matters more as you scale.

That's the real lesson nobody emphasizes enough. Cold starts aren't actually about Lambda being slow - they're about how you structure initialization. Your experience matches mine. I moved a Rust Lambda function from creating connections per-request to using lazy_static for DB pools. Went from 2.1s cold start to 280ms. The runtime itself wasn't the bottleneck, initialization was. Provisioned concurrency is just band-aid over poor design. You're paying for always-on instances when the real problem is doing expensive work in handler scope. The tradeoff: module-level initialization means more careful state management and harder testing. But that's a better problem than customers timing out.