SpeqForgeWhen "Figure It Out" Becomes the Default
There's a pattern common in product organisations that nobody likes to talk about. A PM writes a spec. The spec is vague in the places that matter most. An engineer asks a clarifying question. The PM is in three meetings and responds hours later with something equally vague. The engineer makes a judgment call. The PM reviews the PR and says "that's not quite what I meant." The engineer sighs.
Repeat this enough times and you get an engineering team that has lost faith in the spec process. They stop reading specs carefully because experience has taught them the specs won't answer the questions that actually come up during implementation. They start making more autonomous decisions, not because they want to own the product direction, but because waiting for product to clarify is slower than guessing.
This is the engineering-product gap. It's not primarily a communication problem. It's a trust problem, built up over many sprints of inadequate specs.
What Engineers Actually Need From a Spec
After talking with engineering leads at a dozen product companies, the list of what engineers actually want from a spec is shorter and more specific than most PMs expect:
1. A clear definition of done. Not "implement CSV export" but "a user can click Export on any results view, receive a downloaded CSV file within three seconds, with all visible columns included, formatted so it opens correctly in Excel and Google Sheets without transformation."
2. Edge cases pre-decided. What happens when the dataset has 100,000 rows? What if the user has no results? What if they're on mobile? Engineers don't need exhaustive answers to all of these, but they need to know whether the PM has thought about them or whether they're going to be surprised by a Slack message two days before launch.
3. The why, not just the what. Knowing that CSV export is needed because enterprise customers do their own analysis in Excel fundamentally changes how an engineer approaches implementation choices. It affects column naming, data formats, encoding decisions. Context is not padding, it's specification.
4. Explicit non-goals. A spec that says "we are not solving for Excel formula preservation in this version" is worth its weight in gold. It prevents engineers from over-engineering and gives them permission to make simpler implementation choices.
The Trust Cycle
Good specs create trust, and trust creates better specs. When engineers know that specs are reliable, they engage with them seriously. They raise architectural concerns early. They push back on scope in useful ways. They think about edge cases before implementation rather than during.
When specs are unreliable, engineers route around them. Product decisions get made on the fly by whoever is available. Scope creep happens because nothing was clearly defined. The PM spends more time answering implementation questions and less time doing discovery, which means the next batch of specs is even more vague.
Breaking this cycle requires a deliberate investment in spec quality. Not more process, just better outputs. Specs that answer the questions engineers will actually ask.
Practical Changes That Work
Write acceptance criteria before you write the story. This forces you to think about what done looks like before you commit to building it. Many stories that seem clear become murky as soon as you try to write testable criteria. Better to discover that during spec-writing than halfway through a sprint.
Review specs with an engineer before sprint planning. A 15-minute walkthrough with one engineer, before it enters the sprint, will surface 80 percent of the implementation questions. Answer them in the spec. The sprint meeting becomes a confirmation rather than a clarification session.
Track spec quality as a metric. Count mid-sprint PM questions per story. A well-run product team should generate fewer than one clarification question per story in sprint. If you're averaging three or four, the specs are not doing their job.
Celebrate when engineers say "I built exactly what the spec said." This sounds trivial, but the feedback loop between spec quality and team culture is real. When engineers know that good specs get recognised, they ask for them. When PMs know that good specs reduce their interrupt-driven workload, they invest in writing them.
The engineering-product relationship is the most important one in a product org. It runs on trust. Specs are one of the primary ways that trust is built or eroded, one story at a time.