Regardless of the size of your team, sharing information effectively is essential. Your teammates need access to the information necessary for them to do their jobs well. While many tools and processes can support this, not every technique will suit every team. Company culture, team structure, and maturity all play a role.
I’ve worked in the software industry for 11 years, spanning startups, scale-ups, and large enterprises. Through this experience, I’ve seen how quickly knowledge erodes when teams scale, product complexity increases, and tech debt goes untracked. Critical context disappears with staff changes. Decisions get buried in Slack threads. Features become “owned” by memory instead of documentation.
The result? Engineers lose hours digging through code or chasing down teammates to explain obscure logic that no one fully remembers.
Having worked as both a software engineer and, more recently, a team lead, I’ve experienced this problem from the individual contributor and leadership perspectives. I’ve also seen how intentional knowledge-sharing systems reduce onboarding time, eliminate bottlenecks, and restore engineering velocity.
In this article, I’ll outline proven techniques for scaling knowledge across engineering teams — including product documentation, tech lead office hours, video-enhanced PRs, and structured knowledge-sharing. For each technique, I’ll also share one example of an AI tool that can help you reduce some of the manual load and more easily integrate these tactics into your day-to-day operations.
These approaches aren’t theoretical; they’ve delivered results in real-world teams and can do the same for yours.
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Storing product documentation in tools like Confluence is one of the most popular techniques for sharing information. When maintained well, they can be a great manual of your product — helping engineers, customer support, and product managers find answers quickly.
For this technique to be effective, however, it must be implemented within the culture. Many developers roll their eyes at documentation. And perhaps rightly so. It’s often outdated, incomplete, or hard to navigate. And without a strong culture of maintenance, it stays that way.
I once worked for a healthcare company. Their product, among other things, offered customers the ability to search and book appointments with doctors. Doctors regularly called customer support, confused by how our appointment search worked. We decided that if a customer had a question, the answer should already exist in the product documentation. If it didn’t, support would open a bug ticket. When a developer picked it up, even if the issue wasn’t a bug, they had to update or link to the documentation before closing it.
This forced us to treat documentation as a product asset, not an afterthought. It also motivated our team to build internal tools so customers could find answers themselves, reducing the support load and bug backlog.
Pro tip — Embed documentation updates into your “definition of done.” That’s how you make documentation a living system, not a forgotten wiki.
AI tip — In my current company, we have dedicated ask-[product-area] Slack channels where coworkers can ask questions or raise bugs. In the last 6 months, we have added a Slack-Rovo integration where an agent who has access to our documentation will start a thread and answer for the team. A human can always step in and add more details, but at least the agent has done the first pass. The more thorough your documentation is, the more effective an AI agent will be at uncovering that information for you.
Imagine being assigned a bug, diving into the code, executing a git blame to see the latest change, and ending up at a GitHub PR page with 0 descriptions. All that’s left is for you to read the code change to understand why it was done this way. Many developers have run into this issue and felt the frustration.
Well-crafted PR templates can help. By standardizing what developers should include — such as change descriptions, screenshots, or links to related tasks — you make your codebase easier to navigate and debug later on.
I would even suggest taking it a step further for distributed teams by including video recordings. I once worked with an education startup that was fully distributed to developers in both Europe and South America. Getting everyone to work at the same time wasn’t always possible, and we wanted to avoid the back-and-forth that can occur when a PR is created, and the reviewers don’t understand a change.
To prevent this, we started creating video recordings to explain the code change and include them in the pull request template. Developers would walk through their changes, explain the why behind their decisions, and highlight anything reviewers should watch for.
I strongly recommend implementing pull request templates as soon as possible. Regardless of the size of your team, documenting code changes will help streamline code review, track the code version, and make debugging simpler.
Get started — See the many PR templates created by Steve Mao, including bug issues or new features, to help you get started.
AI tip — When conducting code reviews, context is crucial to understand the issue the pull request is trying to solve. Any documentation provided by the developer or AI helps the developers complete their reviews. GitHub code review has been quite helpful in surfacing issues with pull requests and speeding up the code review process by providing additional context.
See GitHub’s Copilot documentation to see how it works with or without a license.
Engineers can easily get siloed, especially in larger companies or those with many parallel projects. If your company doesn’t have a regular meeting time where engineers can learn what other people are doing, you lose a precious opportunity for them to collaborate and share ideas.
Knowledge-sharing sessions help break down those walls.
These sessions come in many forms — lunch-and-learns, project postmortems, or 15-minute lightning talks. The format matters less than the consistency and usefulness of the content.
At one company, I led a project that moved our email system from AWS Pinpoint to Braze. After it wrapped, I hosted a one-hour session for the entire engineering team. I covered the new system’s capabilities, how we warmed IPs, and some Google Analytics tracking quirks we discovered.
Many devs who hadn’t touched marketing tools before found it enlightening — and it sparked a series of future talks on other projects.
Pro tip — Encourage short, informal presentations for teams new to this practice. As comfort grows, you can graduate to more structured sessions across teams or departments.
AI tip — Use a notetaker tool to have a written version of your presentation. Then, include it with the video in your internal technical documentation so AI agents can surface it when searching for answers to queries.
Product documentation supports user and cross-functional teams, but technical documentation is built by and for engineers. This type of documentation typically covers dev environments, deployment processes, service ownership, and — crucially — technical debt.
When I worked at the healthcare company, we dealt with significant technical debt. I remember a particular case where a coworker and I mapped and documented a particularly complex bug. Instead of patching it and moving on, we documented the entire scenario, proposed a few solutions, and shared it with the broader team. That write-up became a reference for similar issues down the line.
Even if a fix doesn’t get prioritized, documenting it builds a paper trail that outlives staff changes. It also gives engineers — especially those aiming to become seniors — a way to demonstrate ownership and strategic thinking.
You could even create a space just for tech debt write-ups. You’ll be thankful the next time someone asks, “Why haven’t we fixed this yet?”
AI tip — If you use Atlassian products, once you have documented your findings, you can use Rovo AI to create a JIRA work item from your page. Then you can add this item to a tech debt epic to be prioritized in the roadmap.
I have only seen this technique implemented once in a distributed, fully asynchronous company.
The idea is simple. Tech leads dedicate a specific block of time for questions or pair programming sessions with the rest of the team. Instead of fielding numerous one-off requests, they could block periods of time for questions and then focus on their work the rest of the day. This technique improves accessibility and ensures that engineers don’t have to wait for a tech lead to be available.
This format works especially well in remote-first teams, where quick Slack pings can easily pile up. Office hours offer predictability for the team and focus for the lead.
You could even rotate responsibility across senior engineers or theme different office hours (e.g., “DevOps Thursdays” or “Data Fridays”).
Knowledge sharing isn’t just about writing things down, especially as you are scaling.
The techniques in this article aren’t one-size-fits-all. Start with one or two that fit your team’s size and culture. I’ve also built this quick summary table so you can get started:
| Technique | Best for | Team size | Team environment fit | Key benefits | Pro tips |
|---|---|---|---|---|---|
| Product documentation | Establishing a central knowledge source | Small to large | Works well in process-driven, detail-oriented, async teams | Improves onboarding, supports customer-facing teams | Needs continuous updating; embed into workflows |
| Pull request templates and videos | Making code reviews clearer and asynchronous | Medium to large | Ideal for distributed, async-first teams with code ownership | Streamlines handoff, reduces review friction | Use short videos; enforce template usage |
| Knowledge-sharing sessions | Promoting cross-team learning and alignment | Small to medium | Fits collaborative, communicative, hybrid, or in-person teams | Builds culture, encourages peer learning | Make it regular; offer quick, informal formats |
| Internal technical documentation | Retaining technical and architectural context | Medium to large | Good for teams that value planning, ownership, and structure | Helps track tech debt, aids continuity | Document major decisions, not just how-tos |
| Tech lead office hours | Providing structured mentoring and support | Medium | Best for async or remote teams with a culture of accessibility | Encourages accessibility, reduces interruptions | Timebox wisely; rotate responsibilities if needed |
Over time, you’ll build a knowledge-sharing system that not only supports current teammates but also sets up future ones for success.
Editor’s note: This post was updated in March 2026 to include examples of AI strategies to help enhance each of the knowledge circulation tactics.
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