Hey everyone, I wanted to bounce this off the group because I'm starting to see a pattern in my client work. I'm wrapping up a pretty complex Salesforce-to-HubSpot migration where we're building a lot of custom objects and middleware in TypeScript, and I've been leaning on Claude Code heavily for the implementation phase.
Here's my growing concern: while Claude is incredibly fast at generating *functional* TypeScript code, I'm finding the suggestions often lack the nuance and specificity that comes from deep domain experience. It feels like I'm getting solid "textbook" answers, but not the "battle-tested" solutions I need.
For example, when I asked for a function to validate and transform incoming lead data from an old CRM format to the new one, Claude gave me a perfectly valid TypeScript function with a generic interface. But it missed the crucial, gritty details that actually matter in production:
* No consideration for batch processing implications when dealing with 10,000+ records – everything was synchronous.
* The error handling was a simple `try/catch` that logged and threw, with no strategy for partial failures, retry logic, or dead-letter queues.
* The type definitions (`interface IncomingLead`) were too permissive. In reality, we know certain legacy fields are *always* present but null, and others are optional in a very specific way. Claude didn't infer those constraints from my prompt; it just made everything optional or used generic types.
* Zero instrumentation. No suggestion for adding logging decorators or performance metrics, which is non-negotiable for client work where you need to prove stability.
It's like getting a beautifully constructed skeleton of a house, but without any of the electrical wiring or plumbing rough-ins. I have to go back and ask a series of increasingly specific follow-up prompts to get to the level of detail required: "Now add retry logic with exponential backoff," "Make the error handling distinguish between validation errors and system errors," "Create a stricter type that reflects our actual legacy schema."
This isn't a deal-breaker – the speed is still a massive advantage – but it does add a non-trivial amount of "architectural oversight" time on my end. I'm constantly having to steer it away from happy-path examples and towards the messy reality of integrated systems.
My question for you all: Are you seeing this in your domains? Is this just the nature of working with a generalist AI on specialist tasks? Or are there prompting techniques you've used to force Claude to think more like a senior dev with scars from failed deployments? I'm particularly interested in how this plays out in:
* Integration patterns (API rate limiting, idempotency keys)
* Data migration scripts (data integrity checks, rollback strategies)
* Workflow automation (state management, long-running process compensation)
Maybe I'm expecting too much, but I feel like the jump from generic to truly robust is where the real implementation time gets spent. Love to hear your thoughts and experiences.
Implementation is 80% process, 20% tool.