Clear, concise, and actionable development tickets that help your team move faster with fewer blockers.
If you’ve ever been deep in a planning session and realised no one really knows what a ticket is about - or a developer is needing to ask you a question every five minutes on implementation - you already understand why writing good stories matters. A well-written ticket is not just documentation - it’s a communication tool, a guide, and an accelerator.
It helps everyone: developers, testers, designers, stakeholders, even your future self. Done right, it prevents ambiguity, saves time, and builds trust across your team.
Consistency helps your team know what to expect.
Over the years, I've put a lot of thought into how I write tickets. Whether a story is being picked up for the first time or revisited months later, having a clear and consistent structure helps everyone get on the same page quickly.
Before we dive into the details, here’s a quick overview of how I typically structure a development ticket:
Each section serves a different purpose, but together they make for a well-rounded, easy-to-follow ticket.
Here’s a practical breakdown of how to write a user story that’s effective and realistic, especially from the perspective of a Product Manager or Product Owner:
Use the classic structure:
"As a [type of user], I want [to do X] so I can [get Y outcome]."
If your feature has multiple user stories, list them out. Each helps clarify the who, what, and why. It doesn’t really matter how many additional ones are added, but normally I would say a maximum of five. If you find yourself listing more than that, it might be a sign the story needs to be broken down into smaller, more manageable parts. You could also be trying to solve too many problems in a single ticket. And remember, you don’t need to capture every possible user story—just the key ones that are most relevant to this particular feature, so the team can stay focused on what matters most.
Start your story with the background:
Context isn’t fluff - it’s what helps a developer (or designer, analyst, etc.) understand the thinking behind the story.
I usually label this section as Business Context. It’s where I’ll also include how users are currently using the system. Even if the existing business process you're automating or adapting is a manual one, describing it can still give useful context to the team. For example: “When the sales agent makes a sale, they enter the sales amount, description, etc. into X file (see attached). This allows the sales manager to keep track of all sales.”
Add helpful facts that provide framing.
This section is where the spark solution would go - it’s where I’d record who suggested the idea and what their proposed solution was. If I had my own version of the solution, I’d put that underneath theirs so we could see how it evolved. It helped keep track of the decision-making process and allowed others to understand how different solutions were considered. When the ticket is ready to be developed, I usually move this section to the bottom of the ticket under a heading called History, or move it into Confluence with a link in the ticket, so it doesn’t confuse the actual implementation path.
Clearly explain what the solution should do:
This is the solution you want the developers to actually create.
The goal is clarity - what is someone building, and how should it behave?
Split this into:
Must Have - These are your hard requirements. Think of them as pass/fail conditions. If any of these criteria are not met, the feature should not be released until they are.
Nice to Have - These are low-priority additions that shouldn’t block delivery. After the feature is released, I usually take any unfinished nice-to-have items and move them into a new ticket for the backlog. These are great for quieter times or for onboarding less senior developers who can pick them up as manageable, lower-priority tasks.
Example:
In some teams, you might use two checkbox columns - one for developers, one for QA. This works best when everyone’s aligned. That said, this level of overhead really only works in corporate environments where things move slower and there’s a well-defined QA process. In startups, scaleups, or fast-paced companies, I’d avoid trying to enforce this - it's likely to slow things down rather than help.
Always link:
This helps future you (and everyone else) see how the work fits together.
Some features are fuzzy at the edges. You might not have all the answers. That’s okay.
When uncertainty exists:
If you don’t have time for a spike:
Jira has a history log, but it’s not always user-friendly - so spell out major updates in plain sight.
Be careful not to overstep:
Missing edge cases is a fast track to scope creep and frustration.
Edge cases often change the implementation approach - better to know that up front.
Tickets that are too big cause problems:
Aim to keep tickets small:
Example: Building a marketing site
Instead of writing one big story like "Build a marketing website with a blog and waitlist", break the work down into smaller stories like this:
This allows early releases, faster feedback, and better morale.
Design stories are usually structured a little differently than developer tickets. They focus more on user journeys and goals - what the user is trying to achieve and how they might interact with the system. It’s also important to outline how the design should behave across different devices and screen sizes, with a mobile-first mindset if you're working on web products.
You should clearly define the level of fidelity expected - whether this ticket is for wireframes, high-fidelity mockups, or a clickable prototype. And if it's a prototype, make sure to state who it’s for (e.g. internal use, stakeholder review, or user testing).
I also include any relevant A/B test insights or examples of how competitors solve similar problems. Finally, clearly outline both the happy path and any potential unhappy paths so the designer knows what scenarios they need to consider.
When writing stories for data teams, it’s important to distinguish between data scientists and data analysts, because their work often follows different paths.
Data scientists usually take on goal-driven work that is exploratory in nature. These tickets tend to resemble spikes more than structured stories, since there might be many ways to solve the problem or approach the analysis.
Data analysts, by contrast, often work with well-defined tasks and expected outputs. Their tickets are usually closer to development stories and could include things like producing a dashboard, generating a specific report, or pulling analysis on a user segment or behavioural trend.
PMs and POs tend to write stories that focus on outcomes, user context, and business value. They aim to capture the reasoning behind the request and the user needs it addresses, making the stories more vision-driven and less bogged down in excessive detail.
Business Analysts, on the other hand, often craft tickets that are more focused on specific requirements, constraints, and system behaviours. Their stories might include detailed technical rules, performance expectations, and any regulatory or compliance needs that must be considered.
You’ll often find overlap between the two styles, but the focus is different. We’ll explore this distinction more deeply in a future blog.
✅ Do:
❌ Don’t:
One last note: your ticket doesn’t need to be fully written the moment the idea surfaces. In fact, that’s rarely how I work. I usually start with the Business Context and the Proposed Solution (Initial) as soon as the spark for the idea hits. Then, once we’ve decided to move forward with the feature and we have a clearer understanding of what we’re building, I begin to fill out the other sections like the actual solution, user stories, and acceptance criteria.
I talk more about this iterative approach in this blog on implementing lean discovery workflows. It’s part of embracing discovery - writing just enough at each step without creating waste.
Great tickets aren’t about perfect grammar or clever formatting. They’re about:
The more you anticipate your team’s questions, the less time you’ll spend unblocking them later.
Clear tickets are a product manager’s silent superpower.