Uploaded July 2026 | Updated September 2026, 2 weeks ago

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Large codebases don't fit into a single LLM prompt. As projects grow, developers often need to split work into smaller pieces and guide the model with structured workflows.

That doesn't mean you should build an elaborate AI harness from day one. A simple workflow often delivers the biggest wins first. More advanced orchestration only becomes valuable when scale, token costs, or diminishing results make it worthwhile.

Have you found better results by keeping AI workflows simple, or has automation paid off early in your projects?

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