GoKawiilDeveloper finds LLM-generated 'Implementation Decision Records' often too vague to be useful
Will Lachance describes his experience using Implementation Decision Records (IDRs), a practice he devised to get better output from LLM coding agents and keep a durable log of implementation decisions. He found that when given a vague prompt, an agent will often just fill in the IDR with a generic restatement of what it already intended to build, producing a document that is lossy and arguably worse than a plain code diff.
GoKawiil's interpretation of the reporting above, not reported fact.
This suggests that documentation artifacts generated by LLMs can create a false sense of rigor without actually improving reasoning or leaving a meaningful trail for future readers, according to Lachance's own account. It points to a broader challenge for teams adopting AI coding agents: process scaffolding like decision records may need deliberate prompting and human review to deliver real value, rather than being treated as automatic byproducts of agent workflows.
- IDRs were created to both steer LLM coding agents and document implementation decisions
- Vague prompts led the agent to produce generic, after-the-fact descriptions rather than genuine rationale
- The author questions whether such LLM-written records meaningfully outperform a simple code diff
Source: wrla.ch — Will Lachance, 2026-10-12
Published there as: “IDRs as Records of Intent”
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