The closer you get to releasing software, the less useful LLMs become. They tend to go into loops of 'Fixed it!' without having fixed anything.

In my opinion, attempting to hold the hand of the LLM via prompts in English for the 'last mile' to production ready code runs into the fundamental problem of ambiguity of natural languages.

From my experience, those developers that believe LLMs are good enough for production are either building systems that are not critical (e.g. 80% is correct enough), or they do not have the experience to be able to detect how LLM generated code would fail in production beyond the 'happy path'.

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empath75 2 hours ago | parent [-]

This is not my experience with claude code. It does forget big picture things but if you scope your changes well it’s fine.

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symfrog 2 hours ago | parent | next [-]

I would estimate that out of every 200 lines of code that Claude Code produces, I notice at least 1 issue that would cause severe problems in production.

In my opinion these discussions should include MREs (minimal reproducible examples) in the form of prompts to ground the discussion.

For example, take this prompt and put it into Claude Code, can you see the problematic ways it is handling transactions?

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The invoicing system is being merged into the core system that uses Postgres as its database. The core system has a table for users with columns user_id, username, creation_date . The invoicing data is available in a json file with columns user_id, invoice_id, amount, description.

The data is too big to fit in memory.

Your role is to create a Python program that creates a table for the invoices in Postgres and then inserts the data from the json file. Users will be accessing the system while the invoices are being inserted.

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	▲	edgyquant an hour ago \| parent [-]
		What he’s saying is split this up into multiple tasks to create the table, insert the data etc

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ajshahH 2 hours ago | parent | prev [-]

Yes, but knowing how to scope your changes requires a lot of expertise.