LLM Methodology: Treat Output as a Proposal

Choose the job before the model

A model should be assigned a bounded task, not a vague mandate to solve a system. Useful tasks include comparing documented alternatives, drafting a test case, tracing a repository contract, or producing a first-pass explanation for human review. The task should identify the allowed sources, the output shape, and the decision that a person will still make.

Hypler documents the use of OpenAI tools for research and product reasoning and Codex for repository-grounded implementation and evidence. That does not imply either tool has access to a client system, authority to approve an action, or permission to release code. Those boundaries remain explicit.

• Give the model a bounded question and a defined output format.
• Minimize context to what the task actually needs.
• Keep credentials, approvals, and release decisions with people.

Evaluate behavior, not confidence

A fluent answer can still be incomplete, stale, or incompatible with the repository in front of it. Evaluation begins with examples that represent the decision boundary: expected cases, edge cases, known failure cases, and cases where the correct answer is to abstain or request review. The examples should be versioned with the task when they affect ongoing behavior.

OpenAI's evaluation documentation describes evaluation as testing criteria applied to data and model configurations. That framing is useful even when a team is not using a hosted evaluation service: define the criteria, preserve the inputs, inspect the results, and change the system deliberately rather than relying on an anecdotal prompt.

• Measure the behavior that matters to the workflow.
• Include refusal, uncertainty, and escalation cases.
• Review model or prompt changes against the same criteria.

Keep the evidence chain intact

AI-assisted work is easier to trust when a reviewer can see the source inputs, the proposed output, the human decision, and the validation result. That does not require publishing private prompts or customer material. It requires retaining enough context for the responsible team to understand what changed and why.

The practical method is modest: use a model where it is useful, verify against source and tests, and communicate what was actually checked. A generated response remains a proposal until a responsible person accepts it within the system's approval process.