In our DevEx AI tool, we use two sets of survey questions: DevEx Pulse (one question per area to track overall delivery performance) and DevEx Deep Dive (a focused root-cause diagnostic when something needs attention).
DevEx Pulse tells us where friction is. DevEx Deep Dive tells us why it exists.
Let’s take a closer look at Specification. If the Pulse question “Project and task specifications are clear and well-defined” receives low scores and developers’ comments reveal significant friction and blockers, what should you do next?
Here are 10 deep dive questions you can ask your developers to uncover the causes of poor specification clarity, along with guidance on how to interpret the results, common patterns engineering teams encounter, and practical first steps for improvement. This will help you pinpoint what’s causing the problem and fix it on your own, or move faster with our DevEx AI tool and expert guidance.
The real question is: Do we start with enough clarity, or figure it out while building and pay later?
Deep dive questions should help you map how specification clarity flows through your delivery process and identify where it breaks down:
Meaning → Direction→ Readiness→ Authority→ System Awareness→ Time Integrity→ Cost
Here’s how the DevEx AI tool helps uncover this.
What was unclear too late? – open ended question
Info / What was unclear or changed after work had already started?
Do we know what we’re building and what “done” means?
Can we start without guessing?
Once we start, does it mostly stay the same?
Do we know what’s already decided before we start?
Do we know who this might affect before we start?
How much time is lost?
Ideas to spot or reduce friction?
Do we start with enough clarity, or do we figure it out while building and pay later? Here’s how the DevEx AI tool helps make sense of the results.
Question
What this section tests
Where clarity breaks in real life — not in theory.
It tests:
Questions
What this section test
Whether teams know:
This is direction clarity.
How to read scores
Problem ↓, Done ↓
→ Work feels mechanical. Teams build tasks, not outcomes.
Problem ↑, Done ↓
→ Teams know the “why” but not what good looks like.
Problem ↓, Done ↑
→ Output is defined, but the underlying problem is unclear.
Key insight
If people don’t know the problem or what “done” means, rework is almost guaranteed.
Questions
What this section tests
Whether teams must guess, assume, or fill in missing pieces.
This is practical readiness.How to read scores
Detail ↓, No gaps ↓
→ Teams are building and discovering at the same time.
Detail ↑, No gaps ↓
→ Specs look detailed, but important pieces are still missing.
Detail ↓, No gaps ↑
→ Lightweight specs, but stable shared understanding.
Key insight
Guesswork today becomes rework tomorrow.
Questions
What this section tests
Whether clarity survives contact with reality.
This is time stability.
How to read scores
Stable ↓, Explained ↓
→ Chaos. Work shifts without explanation.
Stable ↓, Explained ↑
→ Change is frequent but at least visible.
Stable ↑, Explained ↓
→ Rare change, but confusing when it happens.
Key insight
Change is normal. Unexplained change creates frustration and waste.
Questions
What this section tests
Whether ambiguity gets absorbed by development.
This is decision clarity.
How to read scores
Clear ↓, Owner ↓
→ Development becomes the decision-maker by default.
Clear ↑, Owner ↓
→ Decisions exist, but no clear authority to resolve new ones.
Clear ↓, Owner ↑
→ Authority exists, but decisions are not prepared in advance.
Key insight
Unmade decisions don’t disappear — they move downstream.
Question
What this section tests
Whether clarity is local or broader.
This is impact awareness.
How to read scores
Known ↓
→ Cross-team impact discovered late.
Known ↑
→ Broader thinking exists before starting.
Key insight
Local clarity is not enough if impact is discovered later.
Question
What this section tests
The real economic cost of unclear specs.
How to read scores
0–1 hr → Minor friction.
1–3 hrs→ Noticeable but manageable.
3–5 hrs→ Structural clarity issue.
6+ hrs→ Clarity failure is a system problem.
Key insight
Hours lost are the most honest metric in the survey.
Ideas to spot or reduce friction?
How to read responses
Look for:
Key insight
If suggestions repeat, people already know where to fix it.
Why & Done ↓
Enough to Start ↓
Effort ↑
Interpretation:
Work starts before clarity exists.
Stability ↓
Effort ↑
Interpretation:
The main problem isn’t starting — it’s constant change.
Decisions ↓
Stability ↓
Enough to Start ↓
Interpretation:
Unmade decisions move into development.
Why & Done ↑
Impact ↓
Interpretation:
Team understands the task, but not system consequences.
Detail ↑ but Effort ↑
→ Specs look detailed, but detail ≠ clarity.
Stable ↑ but Enough to Start ↓
→ Work doesn’t change much, but starts too early.
Owner ↑ but Clear ↓
→ Authority exists, but decisions aren’t prepared early.
Why & Done ↑ but Stability ↓
→ Clear intent, but unstable priorities upstream.
What NOT to say
Those statements trigger defensiveness.
What TO say (use this framing)
“This shows where clarity breaks — before we start, while we work, or after changes.”
“We are not measuring documentation quality. We are measuring how often teams must guess.”
“The cost is not confusion — it’s time lost every week.”
Show only three things:
Then say: “If we improve clarity before starting, we reduce weekly rework hours.”
Everything else explains those three numbers.
Here’s how the DevEx AI tool will guide you toward making first actions.
Problem: Things become clear only after work has started.
First Step:
Create a simple recurring ritual:
“What did we realize too late this sprint?”
Do this at the end of every sprint for 4 weeks.
Then:
Small rule:
If the same “late realization” appears twice → add one upstream check for it.
No templates yet. Just pattern detection.
Problem: Teams build tasks, not outcomes.
First Step:
Before work starts, add one line:
If those two sentences are hard to write → the work is not ready.
Do not add documents. Add clarity in 2 sentences.
Problem: Guessing at kickoff.
First Step:
Introduce a 10-minute pre-start check:
Ask the team:
“What would we be guessing about if we start now?”
If more than 2 major guesses appear → pause.
This doesn’t block agility.
It prevents avoidable rework.
Problem: Change mid-work.
First Step:
When change happens, require:
That’s it.
No long process. Just make change visible.
Stability improves when change becomes explicit.
Problem: Dev absorbs ambiguity.
First Step:
Visible decision rule:
“If this is unclear, who decides?”
Write one name per area.
When question appears → escalate immediately, not after 3 days of guessing.
Speed of decision > perfection of decision.
Problem: Late cross-team discovery.
First Step:
Add one pre-start question:
“Who could this accidentally break?”
If no one knows → that’s the signal.
No coordination meeting yet.
Just make impact thinking mandatory.
Problem: Time loss invisible.
First Step:
Track the weekly number publicly.
Do nothing else.
Just show:
“We lost 4 hours this week to unclear specs.”
Visibility alone drives behavior change.
Symptoms:
Enough to Start ↓
Effort ↑
First Step:
Introduce lightweight “Ready Enough” rule:
Must have:
That’s it.
Symptoms:
Stability ↓
Effort ↑
First Step:
Separate:
If change after start → explicitly call it out as “scope shift”.
Naming change reduces hidden frustration.
Symptoms:
Decisions ↓
Stability ↓
First Step:
Create one escalation lane:
Questions unanswered > 24h → auto-escalate.
Ambiguity dies when it has a clock.
Symptoms:
Impact ↓
First Step:
Add a simple MS Teams habit:
Before starting major work:
“Heads up — this might affect X.”
No formal process yet.
Just signal early.
Detail ↑ but Effort ↑
Step:
Stop adding detail.
Ask: “Which detail actually prevented rework?”
Optimize usefulness, not volume.
Stable ↑ but Enough to Start ↓
Step:
Don’t fix stability.
Fix readiness check.
Work doesn’t change much — it just starts too early.
Owner ↑ but Clear ↓
Step:
Move decision conversation earlier.
Owner exists — use them before sprint.
Why & Done ↑ but Stability ↓
Step:
Freeze goal per sprint.
Allow scope shift only between sprints.
Tiny boundary. Big effect.
Clarity must move left.
Every hour of clarity added before work saves 3–5 hours later.
But:
Do not add process.
Add small decision points.
Small explicit checks > heavy governance.
If you can only do ONE thing:
At sprint planning, add this question:
“What would hurt most if this changes mid-sprint?”
If the answer is:
Then clarify that first.
Why this works:
One question. Huge leverage.
What you’ve seen here is only a small part of what the DevEx AI platform can do to improve delivery speed, quality, and ease.
If your organization struggles with fragmented metrics, unclear signals across teams, or the frustrating feeling of seeing problems without knowing what to fix, DevEx AI may be exactly what you need. Many engineering organizations operate with disconnected dashboards, conflicting interpretations of performance, and weak feedback loops — which leads to effort spent in the wrong places while real bottlenecks remain untouched.
DevEx AI brings these scattered signals into one coherent view of delivery. It focuses on the inputs that shape performance — how teams work, where friction accumulates, and what slows or accelerates progress — and translates them into clear priorities for action. You gain comparable insights across teams and tech stacks, root-cause visibility grounded in real developer experience, and guidance on where improvement efforts will have the highest impact.
At its core, DevEx AI combines targeted developer surveys with behavioral data to expose hidden friction in the delivery process. AI transforms developers’ free-text comments — often a goldmine of operational truth — into structured insights: recurring problems, root causes, and concrete actions tailored to your environment.
The platform detects patterns across teams, benchmarks results internally and against comparable organizations, and provides context-aware recommendations rather than generic best practices.
Progress on these input factors is tracked over time, enabling teams to verify that changes in ways of working are actually taking hold, while leaders maintain visibility without micromanagement. Expert guidance supports interpretation, prioritization, and the translation of insights into measurable improvements.
To understand whether these changes truly improve delivery outcomes, DevEx AI also measures DORA metrics — Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Recovery — derived directly from repository and delivery data. These output indicators show how software performs in production and whether improvements to developer experience translate into faster, safer releases.
By combining input metrics (how work happens) with output metrics (what results are achieved), the platform creates a closed feedback loop that connects actions to outcomes, helping organizations learn what actually drives better delivery and where further improvement is needed.
Returning to our topic — specification — you can explore proven practices grounded in hundreds of interviews our team has conducted with engineering leaders.
