Research To Decision

Purpose

Make the reasoning behind a decision inspectable later: what was compared, what evidence supported it, what was assumed, and why the choice was made — not just the conclusion.

When to use

• Choosing between concrete alternatives (technologies, vendors, approaches) with real trade-offs.
• Asked to "research X and recommend one" or to document why a decision was made.

When NOT to use

• The task is open-ended research with no decision at the end ("what's the state of the art in X") — that's fine to do, but skip the decision-matrix/ ADR machinery; just report findings.
• The "decision" has only one reasonable option — say so plainly instead of manufacturing a comparison.

Required inputs

• The decision to be made, stated specifically.
• Known constraints (budget, timeline, team expertise, existing systems).

If the decision is vague ("should we improve our infra"), narrow it to a specific, answerable question before proceeding.

Workflow

1. Define the decision — state it as a specific question with a finite set of options, not an open-ended topic.
2. Capture constraints — budget, timeline, team skills, compliance, existing systems it must integrate with. These often eliminate options before evidence-gathering even starts.
3. Define evaluation criteria — the dimensions that actually matter for this decision (e.g. operational complexity, cost at expected scale, ecosystem maturity, team familiarity), agreed before comparing options so criteria aren't picked to justify a pre-existing preference.
4. Collect reliable evidence — use available research/web tools for external claims (official docs, benchmarks, credible technical sources). Prefer primary sources over blog-post summaries when possible.
5. Separate evidence from assumption — every claim is either backed by a source/citation or explicitly marked as an assumption. Never blur the two.
6. Compare alternatives — score or rank each option against the criteria from step 3, using the evidence from step 4.
7. Identify trade-offs — no option wins on every criterion; state what's given up by choosing each one.
8. Record uncertainties — what wasn't verifiable, what could change the recommendation if it turned out to be wrong.
9. Recommend a decision — the option that best fits the criteria and constraints, with the reasoning explicit.
10. Create an ADR — the durable record (see output contract).

Tool & resource guidance

• Use web search/fetch tools when available for external technical claims (performance characteristics, pricing, community/ecosystem maturity). Never fabricate a citation — if a claim can't be verified, mark it as an assumption or state it plainly as unverified.
• If the decision has a heavy technical-architecture dimension (e.g. choosing a database or messaging system), consider whether system-design-reviewer should evaluate the shortlisted option's fit before finalizing.
• If the decision depends on user/usage data, data-scientist may need to run first to produce that evidence.

Output contract

Three artifacts:

• RESEARCH.md — raw findings with source attribution for every external factual claim.
• DECISION_MATRIX.md — options × criteria, scored, with the evidence each score is based on.
• ADR.md — using assets/adr-template.md: context, decision, options considered, consequences (including trade-offs given up).

Quality checks

• Every external factual claim has a source, or is explicitly marked as an assumption/unverified — no invented citations.
• Criteria were defined before scoring, not reverse-engineered from a pre-decided answer.
• The recommendation explicitly states what's traded away, not just what's gained.
• The ADR is understandable on its own, without the full research doc, to someone reading it in a year.

Edge cases

• No research tools available: proceed using existing knowledge, but mark every claim's confidence level and recommend the user verify time-sensitive facts (pricing, version-specific behavior) independently.
• Only one viable option given the constraints: state that plainly in the ADR rather than inventing a false comparison.
• Decision is highly reversible/low-stakes: keep the ADR brief — the rigor should scale with the cost of being wrong.

References

See references/adr-quality.md for what separates a useful ADR from a box-checking one, references/evidence-sourcing.md for how to handle claims that can't be fully verified, and examples/kafka-vs-sqs-decision.md for a full worked example.