What Engineering Leads Actually Want from Product Specs
We asked engineering managers what makes a spec useful and what makes one useless. The answers were consistent, specific, and not what most PMs expect.
The Engineering Lead's Spec Report Card
| Spec Quality | What It Looks Like | Engineering Impact | Frequency |
|---|---|---|---|
| Excellent | Data-cited, scoped, testable criteria | Builds start immediately, minimal questions | ~10% of specs |
| Adequate | Clear intent, some vague requirements | 1-2 rounds of clarification needed | ~40% of specs |
| Poor | Vague goals, no acceptance criteria, stale data | Multiple clarification rounds, scope creep | ~35% of specs |
| Harmful | Wrong data, contradictory requirements, no scope | Team builds wrong thing, rework required | ~15% of specs |
The PM-Engineering Gap Is Not About Skill
There is a persistent friction between product managers and engineering leads around spec quality. PMs feel they write thorough, thoughtful specs. Engineering leads feel they receive vague, incomplete specs. Both are usually right because they are measuring different things.
PMs optimize for strategic clarity: why are we building this, what problem does it solve, how does it fit into the roadmap. Engineering leads optimize for implementation clarity: what exactly needs to happen, how do we know it is done, what are the edge cases, what are we explicitly not building.
A spec can be strategically excellent and implementationally useless. A beautifully written narrative about improving the user onboarding experience, with compelling analytics and customer quotes, that never defines what “improved” means in measurable terms, is exactly this kind of spec. The PM is proud of it. The engineering lead does not know what to build.
This gap is not caused by lazy PMs or demanding engineers. It is caused by the tools and processes that produce specs. When a PM writes a spec from memory in a static document, the natural output is a narrative. Narratives are good at communicating strategy. They are bad at defining implementation. The solution is not better PMs. It is better spec-generation processes.
The Five Things Engineering Leads Actually Want
After talking to dozens of engineering managers across companies of different sizes and stages, five requirements emerged consistently. These are not aspirational. These are the minimum bar for a spec that an engineering lead considers “useful.”
1. A clear problem statement with data
Not “users struggle with onboarding.” That is a feeling, not a problem statement. Engineering leads want: “23% of new users drop off at step 2 of onboarding (source: Amplitude activation funnel, last 30 days). Users who complete onboarding retain at 3.2x the rate of those who do not (source: retention cohort analysis).”
The data does two things. It justifies the work (this is worth building because the numbers are significant) and it provides a baseline (we will know the solution works when the 23% improves). Without data, the engineering team is asked to trust the PM's intuition. With data, they can evaluate the opportunity themselves.
2. Verifiable acceptance criteria
Not “the onboarding flow should feel smooth.” That is not verifiable. Engineering leads want: “A new user can complete onboarding in under 3 minutes. The user sees no more than 5 screens. Each screen has a single primary action. Progress is saved if the user leaves and returns.”
Verifiable criteria have a specific property: a QA engineer or automated test can determine, without ambiguity, whether the criterion is met. If the criterion requires someone's subjective judgment to evaluate, it is not verifiable. Engineering leads are not being difficult when they push back on vague criteria. They are trying to prevent the situation where the build is “done” but the PM says it “does not feel right.”
3. Explicit scope boundaries
This is the most underrated element of a good spec. Engineering leads consistently say that knowing what is NOT in scope is as valuable as knowing what is in scope. Without explicit boundaries, scope creep is guaranteed.
A good scope boundary section looks like: “Out of scope for this iteration: mobile-specific onboarding flows, SSO/enterprise authentication paths, onboarding for invited (non-self-serve) users, A/B testing infrastructure for onboarding variants.” Each item should be something that a reasonable person might assume is in scope. If nobody would assume it, it does not need to be listed.
4. Current technical context
Engineering leads want to know what exists today, not just what should exist tomorrow. What is the current onboarding implementation? What technical constraints apply? What dependencies does this work have? What recent changes to the codebase affect this area?
This is where static specs fail most visibly. A PM writes the technical context section based on their understanding of the codebase (which is often incomplete or outdated). The engineering lead reads it and immediately spots inaccuracies. Trust erodes. In contrast, a spec that pulls current technical context from GitHub and references actual code structures is immediately more credible.
5. Data citations, not data claims
There is a critical difference between “our conversion rate dropped 15%” and “our conversion rate dropped from 8.2% to 7.0% between May 15 and June 15 (source: Amplitude checkout funnel, production environment).” The first is a claim. The second is a citation. Engineering leads trust citations. They question claims.
Citations also make specs auditable. If the product decision turns out to be wrong, the team can trace back to the data that informed it and understand what happened. Was the data wrong? Was the interpretation wrong? Was the data right at the time but changed? Without citations, post-mortems become blame games instead of learning exercises.
Why Most Specs Fail These Standards
If the requirements are this clear, why do most specs fall short? Because the process of creating specs makes it almost impossible to meet them consistently.
Time pressure kills thoroughness
Writing a spec that meets all five criteria takes significant effort. The PM needs to pull data from Amplitude, find relevant Slack discussions, review current Figma designs, check the GitHub codebase for technical constraints, and synthesize all of this into a coherent document. In a fast-moving team, the PM often has a few hours to produce a spec. Something gets cut, usually the data citations and the scope boundaries.
Manual synthesis introduces errors
When a PM reads an Amplitude chart, switches to Notion, and writes a description of the chart from memory, information is lost. The exact numbers get rounded. The time period gets approximated. The conditions get simplified. The PM is not being careless. They are doing their best to transfer information between tools that do not talk to each other. The lossy transfer is a structural problem, not a human problem.
Static documents go stale instantly
A spec written on Monday references analytics data from the previous week. By Friday, the data has changed. The design has been updated. A new Slack discussion has added a constraint. The spec is already stale, but it looks current because nothing flags the drift. The engineering lead reads it the following Monday and acts on information that is now a week old.
PMs do not have engineering context
The technical context section is the hardest for PMs to write well. PMs are not engineers. They do not read the codebase daily. Their understanding of technical constraints is based on conversations with engineers, which may be outdated. Writing accurate technical context requires access to current code state, which most PM tools do not provide.
How AI-Grounded Specs Solve These Problems
The five requirements engineering leads want (data-cited problem statements, verifiable acceptance criteria, explicit scope boundaries, current technical context, and data citations) are difficult for humans to produce manually. They are straightforward for an AI system that is connected to the right data sources.
| Requirement | Manual Spec Process | Grounded Spec Process |
|---|---|---|
| Data-cited problem | PM checks Amplitude, writes from memory | System pulls live data, cites exact metrics and time ranges |
| Verifiable criteria | PM writes criteria based on intuition | System suggests criteria based on data patterns and past specs |
| Scope boundaries | Often omitted under time pressure | System identifies related areas and suggests explicit exclusions |
| Technical context | PM describes from memory (often wrong) | System pulls current codebase state from GitHub |
| Data citations | Approximate numbers, no source links | Exact figures with source links and time ranges |
Vantage generates specs with these properties built in. It pulls data from Amplitude, ingests relevant Slack discussions, references current Figma designs, checks the GitHub codebase for technical context, and generates a spec that cites every data point. The PM reviews and refines the output instead of creating it from scratch.
The Real Issue Is Trust
Underneath the specific complaints about vague requirements and missing scope boundaries is a deeper issue: trust. Engineering leads do not trust specs because they have been burned too many times. They have built features based on specs that turned out to be based on outdated data. They have shipped implementations that the PM later said “were not what I meant.” They have dealt with scope creep that the spec did nothing to prevent.
Trust in specs requires two things: accuracy at the time of reading (the information is correct right now) and traceability (every claim can be verified against its source). Static documents provide neither reliably. Grounded, connected specs provide both.
When an engineering lead reads a grounded spec and sees that the conversion rate claim links directly to an Amplitude dashboard, that the acceptance criteria were derived from actual usage patterns, and that the technical context reflects the current codebase, trust builds. Not because they trust the PM more, but because they can verify the spec independently.
What Engineering Leads Can Do About Bad Specs
While this article focuses on what engineering leads want, it is worth addressing what they can do to improve the specs they receive. The PM-engineering relationship is bidirectional, and both sides can contribute to better outcomes.
Define your spec template
Instead of hoping PMs include the right information, give them a template that requires it. A template with mandatory fields for data citations, acceptance criteria, scope boundaries, and technical context sets the bar explicitly. Share this template with your PM counterpart and explain why each field matters.
Do spec reviews, not spec complaints
When a spec is inadequate, the worst response is to complain about it in standup. The best response is to review it formally, the same way you review code. Mark the sections that need more specificity. Flag the claims that lack data. Identify the missing scope boundaries. Treat spec review as a collaborative process, not a judgment.
Advocate for better tooling
If your PMs are writing specs manually in Notion, they will always be fighting the limitations of static documents. Advocate for tools that generate specs from real data, maintain connections to sources, and detect when information goes stale. The tooling investment pays for itself in reduced clarification cycles and prevented rework.
Contribute technical context proactively
PMs struggle with technical context because they are not engineers. Instead of waiting for the PM to get it wrong and then correcting them, proactively share technical constraints, architectural decisions, and code-level considerations before the spec is written. If your PM tools pull from GitHub automatically, even better.
The Bigger Picture: Specs as Shared Interfaces
Product specs are the interface between product and engineering. Like any interface, they work best when both sides agree on the contract: what information will be provided, in what format, at what level of detail, and with what guarantees about accuracy.
The current spec process asks humans to be that interface, manually transferring information between systems and maintaining accuracy through willpower and discipline. It is no surprise that the interface is unreliable. No other critical system in software development relies on manual, undisciplined information transfer.
The future is specs as generated, grounded, connected artifacts. Not because PMs are bad at writing. But because the information that engineering leads need is best assembled from real data sources, not from memory. The PM's expertise shifts from document writing to decision making. The spec becomes a view into the decision graph, not a manually maintained artifact. And the PM-engineering interface becomes reliable because it is backed by live data, not stale prose.
That is what engineering leads actually want. Not better writers. Better systems.