Build a technical decision from traceable sources

Use ChatGPT to organize evidence for a technical choice: adopting a dependency, changing an integration, or deciding whether an agent belongs in a build step. The useful output is a brief another developer can challenge.

OpenAI's research workflow supports synthesis across a declared collection of sources. This guide adapts that method to engineering decisions. Features such as connected sources or research tools depend on the current session.

State the decision before gathering material

Suppose your team is choosing an integration for automated code review:

Decision: Which integration should we trial on a synthetic repository?
Requirements: Explicit model choice, structured findings, cancellation,
              evidence tied to a revision, and a human acceptance point.
Environment: Node.js; Windows development; Linux CI.
Outside this decision: Deployment and a claim that one model is best.
Output: A comparison with documented facts, unknowns, and a bounded trial.

Collect primary documentation for the exact products and versions involved. Include the relevant permission and authentication pages, not just a feature overview. An API reference, a coding agent, and a chat interface can expose different controls even when they use a related model.

Ask for a claim ledger

Use the supplied official sources to compare the integrations against the requirements. For every material claim, give the source URL, section, product and version if stated, and whether it is documented or inferred. Mark missing evidence as unknown. Propose a small trial that could disprove the recommendation.

A practical ledger looks like this:

The table defines the review method. Fill it from the actual current sources; do not turn an empty evidence cell into a guessed product capability.

Separate recommendation from uncertainty

Ask for a short recommendation followed by the assumptions that would change it. A useful trial has a fixed input, expected failure cases, usage limits, and a result that can be rejected.

Have the reviewer open the sources behind the decision-driving claims. A link to a product homepage cannot support a statement about a particular error code. If two sources disagree, record their dates and versions and seek the more specific current reference.

Keep source excerpts brief and attach the claim ledger to the implementation handoff. The coding agent should still inspect the repository and check the chosen version's interface.

What goes wrong

The system mixes old documentation with a new SDK, treats an inference as a documented guarantee, or ranks products using vendor marketing language. A long bibliography can conceal that the central claim has no supporting page.

Narrow the question and check the source that actually carries the claim. Additional research is useful when it resolves a material unknown.

How to check

Pick the three claims that determine the recommendation. Open each source and find the supporting section. Confirm product, version, and date, then test one failure path in the proposed trial.

Ask what evidence would reverse the recommendation. If no result could do so, the brief is describing a preference rather than a useful engineering test.