Skill Comparisons

Some skills produce similar-sounding artifacts. These comparisons help you pick the right one.

PRD vs Solution Brief

The two most common “spec” documents . but they serve different purposes at different times.

	PRD	Solution Brief
When	After solution alignment, before engineering starts	During ideation, before committing to an approach
Audience	Engineering, QA, stakeholders who need to approve scope	Leadership, cross-functional team, anyone evaluating options
Depth	Comprehensive . requirements, scope, technical considerations, timeline	High-level . problem, approach, key metrics, risks
Length	3-8 pages	1 page
Includes	Functional requirements, scope boundaries, dependencies, milestones	Value proposition, key features, success metrics, trade-offs
Produces	An engineering-ready specification	A stakeholder alignment document

Rule of thumb: If engineering could start building from it, it’s a PRD. If you’re still deciding what to build, it’s a solution brief.

Flow: /solution-brief → align → /prd → build

Both frame the “why” . but one is testable and one is descriptive.

	Hypothesis	Problem Statement
Purpose	State a testable assumption with pass/fail criteria	Articulate the problem and why it matters
Structure	”If [action], then [outcome], measured by [metric]"	"Users face [problem] because [cause], resulting in [impact]“
Testable?	Yes . has explicit success metrics and a validation plan	No . it describes, it doesn’t predict
When	After understanding the problem, before designing the solution	During discovery, before committing to a direction
Output	Specific metric targets, experiment design, pass/fail criteria	Problem scope, affected users, business impact, success criteria

Rule of thumb: A problem statement says “this is broken.” A hypothesis says “I bet this specific fix will move this specific number.”

Flow: /problem-statement → /hypothesis → /experiment-design

Both involve mapping external entities . but different ones.

	Competitive Analysis	Stakeholder Summary
Maps	Competitors and their products	People and teams who influence your product
Purpose	Understand market positioning and feature gaps	Understand who cares, what they need, and how to align them
Output	Feature matrix, positioning map, gap analysis	Influence/interest map, profiles, communication plan
When	Early discovery . before defining the problem	Before or during any cross-functional initiative
Audience	Product team, marketing, leadership	PM, project lead, anyone managing stakeholder alignment

Rule of thumb: Competitive analysis looks outward (market). Stakeholder summary looks inward (organization).

Both define expected behavior . but at different levels.

	Edge Cases	Acceptance Criteria
Focus	What could go wrong . failure modes, boundary conditions	What “done” looks like . expected behavior for the happy path and key scenarios
Format	Categorized list of scenarios with expected system behavior	Given/When/Then structured scenarios
Covers	Error states, empty states, concurrent access, data limits	Functional requirements, user-facing behavior
When	After the PRD, during engineering planning	During story writing, before sprint planning
Consumer	Engineering (defensive coding), QA (test cases)	QA (verification), engineering (implementation target)

Rule of thumb: Acceptance criteria say “the feature works when…” Edge cases say “the feature doesn’t break when…”

Flow: /user-stories → /acceptance-criteria → /edge-cases

Both involve measurement . but at different levels.

	Experiment Design	Instrumentation Spec
Purpose	Design the test . what to measure, how to split, when to call it	Define the tracking . what events to fire, what properties to capture
Level	Strategic . hypothesis, variants, sample size, success criteria	Tactical . event names, schemas, triggers, PII handling
Output	Experiment plan ready for review	Implementation spec ready for engineering
When	Before building the experiment	Before implementing tracking code
Consumer	PM, data science, stakeholders	Engineering, analytics platform

Rule of thumb: Experiment design answers “what are we testing?” Instrumentation spec answers “how do we capture the data?”

Flow: /experiment-design → /instrumentation-spec → build → /experiment-results

Both capture learnings . but different scopes.

	Lessons Log	Retrospective
Scope	One specific lesson or incident	An entire sprint, project, or milestone
Depth	Deep . root cause, impact, recommendations, applicability	Broad . wins, improvements, action items across the whole period
When	After a specific event worth documenting	At the end of a sprint or project
Output	Structured lesson with root cause and follow-up	Team discussion summary with prioritized actions
Audience	Future teams facing similar situations	The current team

Rule of thumb: A retrospective asks “how did the sprint go?” A lessons log asks “what did we learn from this specific thing?”