Documentation and Knowledge Management Questions

Situation: We need a lightweight, searchable conflict log to capture disagreements, decisions, rationale, and outcomes so teams can learn and avoid repeating mistakes.

Suggested schema (one row/document per conflict):

• id: UUID
• title: short summary (8–10 words)
• created_at / updated_at: timestamps
• reporter: {name, team, role}
• participants: [{name, team, role, stance}] (stance: pro/anti/neutral)
• context: short description (when, feature/area, release/PR number)
• severity: low/medium/high (impact on schedule/quality/security)
• decision: chosen option (text)
• rationale: bullet list of reasons for decision (technical, business, risk trade-offs)
• alternatives_considered: brief list with why rejected
• artifacts: links[] (PRs, issues, design docs, meeting notes)
• action_items: [{owner, due_date, status}]
• outcome: follow-up notes after implementation (success/failure metrics)
• learnings: concise lessons and recommended guardrails
• owner: team or role responsible for the entry (e.g., tech lead / PM)
• tags: components, release, tech area
• retention_tier: e.g., keep 2 years / archived
• visibility: internal/public/team-only

Who owns logs:

• Creation: whoever raises the conflict (engineer, reviewer, PM)
• Primary owner: team tech lead or rotating “knowledge steward” (quarterly rotation) responsible for ensuring follow-ups, completeness, and tagging
• Platform owner: engineering manager or docs/KB team for tooling and access

Retention policy:

• Active records: retain editable for 12 months
• Long-term lessons: after action_items closed, convert to immutable “lesson” and archive with retention 3 years
• Purge/archive: auto-archive entries older than 3 years; allow export before deletion
• Sensitive items: mark and retain per compliance needs

Process & cadence:

• When conflict arises: create entry within 72 hours; link PR/issue; tag severity
• Decision checkpoint: record final decision & rationale in same entry immediately after decision
• Follow-up: assign action items with owners and deadlines; update outcome within one sprint after implementation
• Weekly lightweight triage in team standup for high-severity/new entries
• Monthly review: tech lead reviews new entries, ensures action items exist
• Quarterly aggregated lessons review (team retro + cross-team forum): present 3–5 patterns, convert repeating issues into checklist items, coding standards, or automated tests
• Annual audit: platform owner reviews tags/trends and proposes process/tooling improvements

Tooling recommendations:

• Store as structured documents in existing KB (Confluence, Notion) or as YAML/JSON files in a repo with a simple UI (e.g., GitHub Issues template + labels)
• Provide templates and a small script/dashboard to surface trends (top tags, recurring components, time-to-resolution)

Example minimal entry:

• title: "DB schema vs. denormalized cache disagreement"
• participants: Alice (DB lead, pro-normalize), Bob (frontend, pro-cache)
• decision: keep normalized schema; add read-side cache for user profile
• rationale: ensures ACID for writes, easier migrations; caching addresses latency
• artifacts: PR #234, design doc link
• action_items: implement caching + monitor latency (owner: Bob, due: 2 sprints)
• outcome: latency improved 30%; no data inconsistency after 6 months
• learnings: add “read-side cache” pattern to architecture playbook

Why this works:

• Lightweight, templated entries reduce friction
• Clear ownership and cadence ensure follow-through
• Structured fields enable search, metrics, and automated trend detection
• Converting repeated conflicts into playbook items closes the learning loop

README (top-level)

• Title, one-line summary, status badge (build, coverage, version)
• Quick Start: install, minimal code snippet to call the module/API, expected response
• Table of Contents linking to sections below
  Overview and Concepts
• High-level purpose, core concepts, audience (maintainers vs consumers), design goals and constraints
  Detailed Usage
• Language-specific examples (Java/Python/JS) with copy-paste snippets and common patterns
• Configuration options with defaults and examples
  API Reference / Schema & Contracts
• Endpoints/functions with signatures, parameter types, return types, errors
• OpenAPI/Swagger or GraphQL schema and downloadable JSON/YAML
• JSON Schema / protobuf definitions with field descriptions and examples
  Diagrams
• Component diagram (where module fits), sequence diagrams for main flows, data model ERD
• PNG/SVG in docs/diagrams and Mermaid source in repo for easy edits
  Migration & Versioning Notes
• Semantic versioning policy, deprecation policy, upgrade guide for each major version
• Breaking-change checklist, automated migration scripts, sample before/after snippets
  Testing & Validation
• How to run unit/integration/contract tests locally and in CI; include contract tests to validate API surface
  Operational Notes
• Rate limits, performance characteristics, monitoring/metrics, rollout/feature-flag guidance
  Troubleshooting & FAQ
• Common errors, debug tips, contact points (owners, Slack), escalation path
  Storage & Discovery
• Primary docs in repo: README.md + docs/ (openapi.yaml, diagrams/, migration-guides/)
• Published site: GitHub Pages or internal docs portal (Confluence) auto-built from docs/
• Registry: published package artifact and API gateway catalog entries
  Maintenance Practices
• Keep examples runnable, add tests that exercise documented examples, PR checklist requiring docs update, CI validation for OpenAPI/schema changes

This structure makes onboarding fast, supports maintainers with technical depth, and ensures changes are discoverable and validated.

An RFC (Request for Comments) is a lightweight formal proposal that documents a design/architecture decision, inviting structured feedback before implementation. It captures motivation, trade-offs, and a proposed plan so stakeholders can make informed decisions and the choice is discoverable later.

When to use RFC vs ad-hoc:

• Use an RFC when the change affects multiple teams, has long-term impact, changes APIs/DB schemas/infra, or requires budget/time trade-offs.
• Use ad-hoc discussion for small, local changes (single-service bugfixes, minor refactors) that won’t affect others.

Minimal contents of a good RFC:

• Title, author, date, and status (draft/accepted/rejected)
• Problem statement and goals (what and why)
• Proposed design/solution (diagrams, API schemas)
• Alternatives considered and trade-offs
• Backwards-compatibility and migration plan
• Rollout/testing/monitoring plan
• Security/perf implications and estimated cost
• Open questions and timeline

Who should review:

• Affected engineers and tech leads for impacted services
• Product manager for requirements/prioritization
• QA/SRE/security when relevant
• Optionally an architecture board or senior engineers for cross-cutting changes

Resolving disagreements:

• Ask commenters to propose concrete alternatives and trade-offs in writing.
• Triage comments: categorize factual issues vs. opinion. Fix factual errors immediately.
• If opinions persist, convene a short meeting to align on criteria (risk, velocity, cost) and use those to decide.
• If still unresolved, escalate to the designated decision-maker (author, tech lead, or architecture review board) with documented pros/cons and recommended choice.
• Record the final decision and rationale in the RFC and mark status to keep the record clear.

Assumption Log Template (required fields with purpose)

• ID: unique short tag (A-001) — quick reference in meetings.
• Assumption statement: concise, testable claim (e.g., “Auth token TTL = 24h”).
• Context / Impact: where it applies and what will break if wrong.
• Owner: person responsible for validation (name/role).
• Confidence (1–5): team's current belief level.
• Validation method: concrete test/experiment (unit tests, load test, user interview, telemetry).
• Validation criteria: measurable pass/fail conditions.
• Evidence / Result: link to test results, tickets, analytics.
• Expiry / Review date: date to re-evaluate or auto-escalate.
• Priority / Risk: Low/Med/High — drives action urgency.
• Next steps: actions, stakeholders, and estimated effort.
• Related backlog items: links to stories/epics affected.

How to integrate into Daily Scrum and Sprint Planning

• Daily Scrum: owner briefly states status (e.g., “A-003: confidence 2, running smoke test today”) — keeps visibility without derailing standup. Any blocking high-risk assumptions flagged for a short follow-up with relevant teammates after standup.
• Sprint Planning: review all assumptions linked to planned stories. For assumptions with confidence ≤3 or High risk, convert validation into explicit backlog tasks or spike stories, estimate effort, and assign owners. Use expiry and priority to decide sequencing (validate risky assumptions early in sprint 0 or first sprint).
• Cadence & Governance: surface the log in sprint review and retro; update evidence and confidence. Keep the log in a lightweight shared location (confluence/README or Jira custom issue type) so it’s discoverable and actionable.

Why this works

• Makes assumptions explicit, testable, and owned.
• Reduces late surprises by forcing early validation and prioritization.
• Fits agile rhythm: quick visibility in daily standups and formalized action during planning.