DEV Community: Duy Cao

Dev-Client Communication that Builds Trust

Duy Cao — Mon, 04 Aug 2025 09:45:40 +0000

Most engineers don’t struggle because they can’t code. They struggle because someone misunderstood the client—and now everyone’s sprint is on fire.

I’m not a developer. I’m a marketer who works closely with product teams building software for real clients. And over the years, I’ve seen one pattern stand out above all: the best software teams don’t just build clean code—they communicate clearly across teams and with clients.

This post is adapted from a conversation with one of the tech leads in my company. It isn’t about email etiquette or vague "soft skills." It’s a breakdown of how experienced dev teams handle misaligned expectations, cross-time-zone clients, and shifting requirements—and still manage to keep trust intact. All based on firsthand insights from tech leads who’ve worked on global, high-pressure projects.

1. The Hidden Cost of Misunderstood Requirements

In every software project, there’s a fork in the road early on: either the client’s needs get crystal clear—or they don’t. And if they don’t, you’re headed toward rework, stress, and lost trust.

Take this insight from a tech lead on a global finance AI project:

“The most important part isn’t the code—it’s product direction. If we don’t understand exactly what the client needs to solve their business problem, everything we build is at risk.”

This isn’t hypothetical. In one past project (SH Fin), unclear product direction dragged out development, lowered code quality, and eventually eroded the client’s confidence. Why? Because the dev team was guessing.

Great teams don’t wait for clients to magically clarify. They proactively ask questions, consult, and help the client define their needs through BA and scoping sessions. As the same lead put it:

“If the client isn’t clear, it’s our job to help them clarify. Otherwise, we’ll build the wrong thing fast.”

💡 My tech lead tip:

Before any sprint begins, confirm requirements in writing, use example flows or visuals, and always restate the goal in business terms—not just features.

2. Communication Is Project Insurance

Even great plans hit unexpected roadblocks. When that happens, silence is the enemy.

In a cross-continent project involving an Australian architect, a South African banking client, and a Vietnam-based dev team, unexpected requirement misalignments popped up mid-sprint. The fix wasn’t just technical—it was communicative.

The lead explained his approach:

Confirm where scope was misunderstood (feature X was built wrong? Why? At what point did it drift?)
Own the deviation and propose a clear recovery plan with timeline and expected fixes.
Get mutual agreement before proceeding—never assume understanding.

“Clients don’t leave because something broke. They leave when we stop communicating and hide what’s wrong.”

Transparency doesn’t just patch trust—it preserves it.

3. Navigating Time Zones and Cultural Gaps

The team worked across Vietnam, South Africa, and Australia. That meant one thing: someone always worked late.

But more important than clock time was clarity. When face time is limited, high-impact communication matters even more:

Meetings focused on major decision points: feature changes, demo validation, sprint changes.
Follow-up messages recapped key takeaways in writing (e.g., “This is what we understood, here’s the next step…”)
Confirming client language expectations early—especially with second-language English users.

It wasn’t just about speaking fluent English. It was about making sure the meaning behind every request was understood and confirmed—a habit that saved hours of rework.

4. Internal Alignment Builds External Trust

When requirements go sideways, it’s not just the devs who feel it. QA teams scramble to re-test. Product managers have to rewrite sprint plans. Stress piles up.

In one scenario, after realizing the product was misaligned with the client’s expectations, the team:

Paused the sprint to realign internally.
Created a lightweight POC to validate the new direction.
Walked QA and devs through each expected outcome.
Communicated the changes transparently with the client—including timeline, why the gap happened, and how it was being fixed.

The result? A product back on track, and a client who appreciated honesty more than a superficial “on time” delivery.

5. What Trust Looks Like When You Get It Right

After this approach became habit, results spoke louder than any process doc:

Clients stayed longer and renewed projects.
Teams worked fewer nights and weekends.
Features shipped closer to scope and timeline.

Trust didn’t come from speed. It came from communication that reduced surprise, clarified change, and involved the client as a partner—not just a ticket generator.

“When we delivered features that aligned with client expectations and deadlines, they trusted us more. That’s when the pressure eased and the collaboration got smoother.”

Conclusion: Technical Skills Get You In. Communication Keeps You In.

You can’t build what your client wants unless you understand what they need—and you can’t understand what they need unless you talk early, clearly, and often.

If you’re a developer, PM, or startup founder working with clients, ask yourself:

Are we confirming expectations in writing?
Do we own up quickly when something’s off?
Are we aligning internally before explaining externally?

You don’t need perfect English. You need precise communication, mutual respect, and clear recovery plans when things go wrong.

Share your thoughts: What's your experience in effective or ineffective client communication?

Translating Tech Speak into Business Value Stories

Duy Cao — Mon, 28 Jul 2025 08:51:35 +0000

Translating Tech Speak into Business Value Stories

For tech-savvy professionals like CTOs, product leads, or technical consultants, the ability to communicate complex development work to business stakeholders isn’t just helpful—it’s essential. Whether you’re pitching a new feature, securing buy-in for infrastructure upgrades, or aligning outsourced teams, bridging the language gap between technical depth and business clarity is what turns effort into impact.

Why “Tech Speak” Fails to Persuade

When developers speak in jargon—APIs, CI/CD, microservices—the business impact often gets lost. This isn’t just a communication problem; it’s a strategic risk. Misalignment between teams leads to:

Project delays from misinterpreted goals.
Underfunded initiatives due to unclear ROI.
Undervalued tech teams that are seen as cost centers, not growth drivers.
Erosion of trust between engineering and leadership.

The Strategic Advantage of Translation

Fluent translation between tech and business enables faster decision-making, smarter investment prioritization, and stronger cross-functional collaboration. More importantly, it positions technology as a strategic lever—not a black box.

Developer Term → Business Value

Here are some key examples:

Scalability: Not just “handling more traffic,” but “enabling growth without increasing costs or risking downtime.”
Agile/Scrum: Instead of “iterative delivery,” it’s “faster response to market changes and reduced time-to-market.”
Technical Debt: More than bad code—it’s “future cost, slower delivery, and increased risk.”
APIs: Beyond integrations—“partnership enablement, ecosystem growth, and new revenue streams.”
Microservices: Not just architecture—it’s “faster innovation, better stability, and lower maintenance costs.”
CI/CD: No longer just automation—“predictable releases, faster user feedback, and developer productivity gains.”
Cloud Migration: More than servers—“cost savings, disaster recovery, and global expansion.”

Shift the Focus: From What to Why

Business leaders don’t care what’s being built until they understand why it matters. Your job isn’t to simplify the tech—it’s to elevate its relevance.

Focus on outcomes: Replace “We’re building a new backend” with “The new backend enables real-time insights, letting sales close deals 30% faster.”
Quantify the impact: “Improved uptime by 0.5%” becomes “$200K/year saved in churn and support tickets.”
Tailor by audience: CFOs want ROI, CMOs want speed to market, COOs care about efficiency. Frame accordingly.

A Simple Framework for Reframing Tech for Business

Know your audience: What do they care about—growth, cost, risk, customer retention?
Translate the concept: Use analogies, avoid acronyms, and connect the dots to real-world problems.
Quantify where possible: Time saved, revenue gained, churn reduced, speed increased.
Structure your story:
- Problem
- Technical solution
- Business benefit
Use visuals: Diagrams and dashboards make abstract systems concrete.

Example:

Instead of “Refactor to microservices,” say:

“Our monolith slows feature delivery and creates risk. Moving to microservices will reduce release times by 30% and improve system uptime, supporting faster innovation.”

Pitfalls to Avoid

Assuming shared understanding: Always clarify, even if you think it’s obvious.
Overloading with detail: Stakeholders want outcomes, not implementation walkthroughs.
Undervaluing your impact: Highlight how each tech initiative drives KPIs.
Speaking down: Be collaborative. You’re not teaching—you’re aligning.

Leading the Cultural Shift

This isn’t just a solo effort. Technical leaders should cultivate a culture of business-oriented communication across their teams.

Train developers to think in business terms—offer templates, workshops, or storytelling frameworks.
Encourage shadowing: Let devs sit in on customer calls or sales meetings.
Model the behavior: As a leader, show how every line of code ladders up to strategic goals.

Final Thought

In today’s digital economy, the true power of technology lies not just in what it does, but in how clearly its value is communicated. Translation isn’t fluff—it’s leverage. When you can speak both code and commercial fluency, you stop asking for a seat at the table—you build the table.

How My Tech Lead Leverages AI Across the Dev Lifecycle (Part 2)

Duy Cao — Mon, 21 Jul 2025 09:51:30 +0000

Welcome back to our series on how our Tech Lead is embedding AI throughout our software development lifecycle. In Part 1, we saw how AI acts as a powerful accelerator in the initial stages—from transforming high-level ideas into actionable user stories, to rapidly generating technical designs, and streamlining task management. The common thread was efficiency, consistency, and freeing up our team for higher-order thinking.

Now, in Part 2, we’ll dive into the heart of software creation: Development, Code Review & Debugging, Testing & QA, and Documentation. This is where AI helps us keep our code clean, catch bugs early, and ship with confidence.

4. Development: Accelerating the Build Process

The Challenge
Writing code isn't just about logic; it's also about boilerplate, repetitive patterns, and remembering API conventions. This "grunt work" can slow down even the most brilliant developers, introduce inconsistencies, and detract from focusing on core business logic. Setting up new projects, adding standard features, or refactoring existing code often involves tedious, manual typing.

How We Use AI
Our Tech Lead leverages AI-powered coding assistants that are integrated directly into our Integrated Development Environments (IDEs). These tools significantly accelerate the build process by handling the mundane and suggesting intelligent completions.

Code Generation: When starting a new feature or adding a common component, developers can use AI tools to generate boilerplate code and scaffolding. For instance, prompting for "a basic REST API endpoint for user authentication in Node.js with Express" or "a React component for a reusable button with specific props" can instantly provide a functional starting point.
Refactoring Suggestions: As codebases grow, refactoring is crucial for maintainability. AI tools analyze existing code and suggest cleaner, more efficient ways to structure functions, rename variables, or simplify complex loops, acting as an intelligent pair programmer.
Boilerplate Scaffolding: For common patterns like setting up database connections, configuring middleware, or integrating third-party SDKs, AI can auto-generate the necessary structure and even fill in basic configuration placeholders.

Tools We Use:

GitHub Copilot: This widely-used AI pair programmer provides real-time code suggestions, autocompletes functions, and even generates entire blocks of code based on comments or function names.
CodeWhisperer (AWS): Similar to Copilot, CodeWhisperer offers context-aware code suggestions, helping developers write code faster and more securely.
ChatGPT (for complex logical chunks): While not an IDE plugin, our developers often use ChatGPT for generating complex algorithms, solving specific coding challenges, or understanding unfamiliar library usage.

Prompt Templates to Try:

For Boilerplate Code:
Generate a Python Flask app structure with routes for /users(GET, POST) and/users/ (GET, PUT, DELETE), using SQLAlchemy for a User model.
Write a basic Vue 3 component for a modal dialog, including props for titleandisVisible, and an emit event for closing.
For Refactoring:
Refactor this Java method to improve readability and performance, ensuring it handles null inputs gracefully: (paste code)
Suggest a more concise way to write this JavaScript array manipulation, avoiding mutable operations: (paste code)
For Algorithm Generation:
Write a recursive algorithm in C# to find all permutations of a string.
Generate a binary search implementation in Go.

The Result: Faster, cleaner code with fewer errors. Our developers spend less time on repetitive typing and more time on solving actual business problems and implementing complex logic. This dramatically speeds up development cycles and ensures a higher degree of consistency across the codebase.

5. Code Review & Debugging: Intelligent Quality Assurance

The Challenge
Code reviews are essential but can be time-consuming and prone to human oversight. Reviewers might miss subtle bugs, logical flaws, or opportunities for optimization. Debugging, especially in complex systems, can be a frustrating and lengthy process of tracing errors through layers of code.

How We Use AI
AI acts as an intelligent assistant in both code review and debugging, helping us identify issues proactively and pinpoint errors faster.

Analyze Pull Requests: Before a human even looks at a pull request, AI tools perform an initial pass. They scan for common anti-patterns, potential security vulnerabilities (e.g., exposed API keys, SQL injection risks), style guide violations, and even suggest logical improvements based on learned patterns from millions of open-source projects.
Suggest Fixes & Refactorings: Beyond just flagging issues, these tools often propose direct code changes. For example, if a loop is inefficient, the AI might suggest a more optimized data structure or algorithm. If a variable is unused, it'll suggest removal.
Contextual Bug Diagnosis: When a bug is reported (e.g., from a test failure or user report), AI tools can analyze logs, stack traces, and relevant code sections to suggest potential root causes or narrow down the search area, significantly reducing debugging time.

Tools We Use:

DeepCode AI (now Snyk Code): Focuses on security vulnerabilities, code quality issues, and compliance.
ChatGPT / Claude / Other LLMs: Developers often paste anonymized code snippets or error messages into these models to get instant explanations, potential fixes, or alternative approaches.
CodiumAI: Generates tests and suggests fixes based on code analysis, helping to improve code quality and identify bugs earlier.

Prompt Templates to Try:

For Code Review Suggestions:
Review this TypeScript code for potential bugs, security vulnerabilities, and adherence to best practices. Suggest improvements for readability and performance: (paste code)
Analyze this Go function for concurrency issues and propose solutions: (paste code)
For Debugging Assistance:
I'm getting this error in my Java Spring Boot application: [Stack Trace Here]. What are the most common causes for this type of error, and how can I start debugging it?
This Python script is producing incorrect output. The expected output is [X] but I'm getting [Y]. Can you help me trace the potential issue? (paste code)

The Result: Shorter review cycles, higher code quality, and faster bug resolution. Our Tech Lead often jokes that AI is like having an army of junior engineers tirelessly scanning every line, allowing the human reviewers to focus on architectural decisions and complex logic.

6. Testing & QA: Smarter, Broader, Faster Coverage

The Challenge
Manual testing is slow, expensive, and error-prone. Even automated tests require significant effort to write, maintain, and ensure comprehensive coverage. Test suites can become brittle, and identifying test gaps often relies on human intuition or post-release bug reports.

How We Use AI
AI is revolutionizing how we approach testing, making our QA processes more efficient, intelligent, and robust.

Auto-Generate Unit/Integration Tests: Based on existing code or function definitions, AI tools can automatically generate relevant unit and integration tests, complete with plausible input data and expected outputs. This dramatically boosts initial test coverage without manual effort.
Analyze Test Gaps: AI analyzes existing test suites against new code changes or known application behavior to identify areas where test coverage is weak or missing. It can suggest new test cases to cover edge scenarios or recently modified functionalities.
UI Test Automation: For user interface testing, AI-powered tools can "understand" visual elements, allowing for more resilient UI test automation. They adapt to minor UI changes, reducing the brittleness often associated with traditional UI automation frameworks.

Tools We Use:

Testim.io: Leverages AI to create resilient UI tests that automatically adapt to changes.
Diffblue (for Java): Automatically generates Java unit tests using AI, significantly accelerating test creation.
QA Wolf: A platform that uses AI to generate and maintain end-to-end tests based on user flows.

Prompt Templates to Try:

For Test Case Generation:
Generate unit tests for this C# function that calculates order totals, including edge cases for zero items, negative prices, and large quantities: (paste function code)
Create integration test scenarios for a user registration flow, covering successful registration, invalid email, existing email, and password strength violations.
For Test Gap Analysis:
Given this set of existing Jest tests for a React component, what additional test cases should I write to ensure full coverage of props, state changes, and user interactions? (paste existing tests and component code)
Analyze this user story: "As a user, I want to upload a profile picture so that I can personalize my profile." What are key test scenarios (positive, negative, edge cases) I should consider for this feature?
For UI Test Automation:
Describe how an AI-powered UI test automation tool would handle a subtle change in button position (e.g., from left to right) without breaking the test for a "Login" button. (This prompt helps understand the concept rather than direct action).

The Result: Broader test coverage, faster test cycles, and fewer escaped defects. By automating much of the test creation and analysis, our QA engineers can focus on complex exploratory testing and validating the overall user experience, rather than writing boilerplate tests.

7. Documentation: Always Up-to-Date & Accessible

The Challenge
Documentation is notoriously difficult to keep current. As code evolves, design documents, API specifications, and onboarding guides quickly become outdated, leading to confusion, errors, and significant time wasted by new hires or developers trying to understand existing systems.

How We Use AI
Our Tech Lead ensures that documentation isn't an afterthought but an integral, living part of our development process, empowered by AI.

Generate API Docs: AI tools can analyze codebases (especially annotated APIs) and automatically generate detailed API documentation, complete with endpoint descriptions, request/response formats, and examples.
Generate Tech Specs: For new features or complex modules, developers can input high-level requirements or code snippets, and AI can help draft technical design specifications, outlining components, data flows, and architectural decisions.
Generate Onboarding Materials: AI assists in creating comprehensive onboarding guides for new developers or product users by summarizing existing codebases, technical specifications, and internal knowledge bases. It can even generate FAQs based on common support queries.

Tools We Use:

Mintlify: A platform that integrates documentation directly into the development workflow and uses AI to generate and improve docs.
ChatGPT / Claude / Other LLMs: Used for summarizing complex technical documents, rephrasing explanations for different audiences (e.g., technical vs. non-technical), or generating basic outlines for documentation.
DocuWriter (or similar tools): Integrates with code to automatically generate technical documentation.

Prompt Templates to Try:

For API Documentation:
Generate OpenAPI (Swagger) documentation for the following Node.js Express endpoint. Assume it interacts with a MongoDB User collection. Include request body schema, response schemas (200, 400), and example values: (paste endpoint code)
Based on this Python function, write a clear docstring that explains its purpose, arguments, return value, and any exceptions it might raise: (paste function code)
For Technical Specifications:
Draft a technical design document outline for a new real-time chat feature in a web application. Include sections for architecture, data models, API endpoints, and scaling considerations.
Explain the technical implications and required changes to our existing microservice architecture if we were to add support for asynchronous event processing using Kafka.
For Onboarding/Knowledge Base:
Summarize the key steps for a new front-end developer to set up their local environment for our dashboard project, assuming they have Node.js and Git installed.
Generate 10 common FAQs about integrating with our payment gateway API, based on typical developer queries.

The Result: Always up-to-date internal documentation. This reduces onboarding time for new team members, ensures consistent understanding across the engineering team, and drastically cuts down on context-switching and clarification efforts. When documentation evolves with the code, the entire team benefits.

Why It Matters: The Cumulative Impact of AI in Dev

The AI tools and strategies our Tech Lead employs aren't just isolated tricks; they form a cohesive, powerful system that cumulatively transforms our development lifecycle.

Speeds up Delivery Without Compromising Quality: By automating mundane tasks, providing intelligent assistance, and catching issues early, we can ship features faster and with greater confidence.

Keeps Teams Focused on Business Logic, Not Grunt Work: Our talented engineers and product managers spend less time on repetitive, low-cognitive tasks and more time on solving complex challenges, innovating, and driving real business value.

Ensures Documentation and Testing Evolve With the Code: AI helps us break the cycle of outdated documentation and insufficient testing, ensuring that these critical components are always aligned with the latest code, making our systems more robust and maintainable.

In Part 3, we'll conclude this series by looking at the broader impact of AI on deployment, monitoring, and future-proofing our entire software development operation. Stay tuned!

How My Tech Lead Leverages AI Across the Dev Lifecycle (Part 1)

Duy Cao — Mon, 14 Jul 2025 10:30:05 +0000

In today’s fast-paced world of software development, the mantra “move fast and break things” is giving way to “move smart and ship clean.” At my company, our Tech Leads have transformed how we build products by embedding AI as a practical accelerator at every stage of the journey—from the first spark of an idea all the way through to deployment. In this three-part series, I’ll illustrate how he does it, starting with Requirement Analysis & User Stories, Technical Design, and Task Management & Planning.

1. Requirement Analysis & User Stories

The Challenge
Requirements often start as high-level ideas in meetings or a half-filled Google Doc. Turning those into clear, actionable user stories can be a painful game of telephone—stakeholder intent gets lost, acceptance criteria go vague, and developers end up guessing.

How We Use AI
We feed AI models with transcribed meeting notes or bullet-point idea lists and prompt them to generate structured epics and user stories. For example, we’ll use:

Prompt:
“Convert these meeting notes into three user‐story statements with acceptance criteria:

Need to track user carbon footprint per transaction
Allow users to set monthly eco‐goals
Send alerts when near goal limit”

The reply we receive:

As a user, I want to see my carbon footprint per transaction so that I can understand the environmental impact of my spending.
Acceptance Criteria:

Display carbon footprint in grams of CO₂ alongside each transaction.

Provide a link to a detailed breakdown view.

As a user, I want to set monthly eco‐goals so that I can manage my carbon budget effectively.
Acceptance Criteria:

Allow the user to define a target in kg of CO₂ per month.

Store and display the current progress toward the target.

As a user, I want to receive an alert when I approach my monthly eco‐goal so that I can adjust my spending.
Acceptance Criteria:

Trigger a push notification when 90% of the eco‐goal is reached.

Provide a link to suggestions for greener spending alternatives.

We then import these into Notion AI or our backlog tool. The result? Faster backlog grooming and clearer stories that everyone—product managers, developers, and QA—understands from day one.

2. Technical Design

The Challenge
Sketching system architecture and validating design decisions by hand can take days. Diagrams get outdated, edge cases slip through, and you end up with patchwork solutions.

How We Use AI
Our Tech Lead uses Mermaid combined with AI-powered plugins (e.g., the “Mermaid-AI” extension) to automatically generate up-to-date diagrams. For instance, we’ll write a quick prompt:

Prompt: “Generate a sequence diagram for payment processing with fraud check, carbon footprint logging, and ledger update.”

participant U as User
participant F as Frontend
participant B as Backend
participant C as CarbonService
participant L as LedgerService
U->>F: Initiate Payment
F->>B: /api/payment with token
B->>C: Log carbon footprint
C-->>B: Carbon footprint recorded
B->>L: Debit user account
L-->>B: Account debited
B-->>F: Payment success
F-->>U: Show confirmation

Within seconds, we have a clean, version-controlled diagram. We can then iterate with prompts like:

“Highlight error-handling for failed fraud checks.”

That auto-generates the updated paths. The net effect is accelerated solution design and better documentation—all stored alongside code in our Git repo.

3. Task Management & Planning

The Challenge
Breaking down features into tasks, estimating timelines, and identifying dependencies is notoriously error-prone—especially when projects scale or new people join teams.

How We Use AI
We leverage Linear AI Assist or Jira Smart Assistants to auto-categorize tasks, suggest story-point estimates, and flag dependencies. Here’s a typical workflow:

Input: “Create tasks for Payment Service feature,” plus a list of user stories.

AI outputs:

Create API endpoint for POST /payments (5 SP, backend)

Integrate CarbonService SDK (3 SP, backend)
Build frontend component for payment form (5 SP, frontend)
Write unit tests for new endpoints (3 SP, QA)
Review: Tech Lead adjusts any estimates or reassigns priorities.

The result? Smoother sprint planning, reduced PM overhead, and fewer surprises mid-sprint. Developers log on Monday and know exactly what to tackle, and PMs can predict delivery dates with more confidence.

Wrapping Up Part 1

In these first three stages—Requirement Analysis & User Stories, Technical Design, and Task Management & Planning—our Tech Lead uses AI not as fluff, but as a practical accelerator that:

Speeds up routine work.
Ensures consistency across stories and designs.
Frees the team to focus on deep engineering and product strategy.

In Part 2, we’ll dive into Development, Code Review & Debugging, Testing & QA, and Documentation—showing you how we keep code clean, catch bugs early, and ship with confidence.