User-Centric Testing Strategies for AI-Generated Code

89% of AI engineers report encountering harmful biases in Gen AI models — yet most testing happens behind closed doors at tech companies. UNESCO released a groundbreaking playbook that changes everything:- “Red Teaming Artificial Intelligence for Social Good”. What is Red Teaming? Think of it as ethical hacking for AI — systematically testing models to expose hidden biases, stereotypes, and potential harms before they reach users. Why this matters NOW:- • 58% of young women globally face online harassment. • AI can amplify these harms at unprecedented scale. • Most vulnerability testing is limited to internal teams. The game-changer:- This playbook democratizes AI testing, giving organizations, researchers, and communities the tools to actively participate in making AI safer. Key takeaways for builders:- -Test for both unintended bias AND malicious exploitation. - Include diverse voices — those most impacted often spot risks others miss. - Use structured prompts to systematically expose vulnerabilities. - Turn findings into actionable improvements. Real example from the playbook:- When testing an AI tutor with identical student performance data, the model gave more encouraging feedback to “David” while suggesting “Chineme” needed external support to succeed. Same data, different gender bias. For anyone building with AI:- This isn’t just about compliance — it’s about ensuring our innovations truly serve everyone. The full playbook is free and includes templates, methodologies, and real case studies. Who’s ready to make AI testing as standard as code reviews? #AI #ResponsibleAI #TechEthics #Innovation #AIforGood #AITesting

Here’s how AI is quietly revolutionizing UAT, and how you can practically use it 👇 𝟓 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐖𝐚𝐲𝐬 𝐀𝐈 𝐂𝐚𝐧 𝐇𝐞𝐥𝐩 𝐁𝐀𝐬 𝐢𝐧 𝐔𝐀𝐓 1. Auto-Generate UAT Test Cases from User Stories Instead of manually drafting dozens of test cases, use AI to quickly generate them based on the acceptance criteria. 🛠️ Prompt for ChatGPT or Claude: "Generate UAT test scenarios and expected outcomes for a user story where a customer logs into an eCommerce app, adds 2 items to the cart, and completes payment via PayPal." Why It Helps: Saves time, ensures full coverage, reduces human error. 2. AI-Based UAT Checklist Generators Don't reinvent the wheel every time. AI tools can create a checklist based on your domain and system type. 🛠️ Use tools like: ChatGPT + Prompt Templates Notion AI Jasper for structured templates Example: "Create a UAT checklist for a mobile banking application with login, balance check, and fund transfer features." 3. Smart Data Input Generators for Testing Need test data like dummy accounts, fake transactions, or synthetic user profiles? AI tools like Mockaroo, DataGen, or OpenAI + Excel plugins can help you generate realistic, varied data instantly. Why It Matters: Testing boundary conditions, edge cases, and data variations becomes faster and smarter. 4. Summarize UAT Feedback Using AI Tired of going through 100s of comments in Excel or Jira? Use Fireflies.ai, Otter.ai, or ChatGPT to: 👉 Summarize stakeholder feedback 👉 Identify recurring issues 👉 Categorize bugs vs. enhancements Example: Paste the exported UAT comments and prompt: "Summarize key pain points reported by testers, group them by module, and suggest root causes." 5. Auto-Generate UAT Reports & Dashboards Gone are the days of manual report writing. Use ChatGPT + Markdown, Notion AI, or Excel AI to create: 👉 Executive summaries 👉 Defect metrics 👉 Sign-off documentation Bonus Prompt: "Create a UAT sign-off report based on the following test results, defect closure summary, and stakeholder comments." Real-Life Example: On a Loan Origination System project, our UAT cycle had over 60 test cases and 8 stakeholders. By using AI-generated test scenarios, feedback summarization, and report automation, we: ✅ Reduced preparation time by 40% ✅ Got faster stakeholder buy-in ✅ Delivered UAT results 2 days ahead of schedule AI isn’t replacing the Business Analyst — it’s empowering us to focus on the strategic and human side of testing: 🗣️ Stakeholder alignment 📈 Business value validation 🎯 Decision-making UAT is where systems meet business reality. With AI as your co-pilot, you can make it smarter, faster, and more reliable. BA Helpline

Here’s the secret to AI-first products: If your AI isn’t where your users already work, it’s just a cool tool they’ll never adopt. Too many teams build standalone apps for developer convenience, only to see low adoption because they disrupt user workflows. Want to create AI that feels like a co-pilot, not a detour? Too many teams treat AI like an add-on instead of designing around how people actually work. If you want your tool to stick, start by testing where and how users will reach for it—not just which feature they like. 1. Watch before you wireframe Shadow your users for days. Note which apps they open first, what data they reference, where they pause. When you map their natural workflow, you can slot your AI into it—rather than forcing them onto a new path. 2. Make the channel your core hypothesis Is the right interface a sidebar in your CRM, a chatbot in Teams, a Slack app, or a push notification on mobile? Instead of asking “is lead-scoring useful?”, test “will sales reps use this inside their CRM?” Show partners quick sketches in each context and see which one they instinctively click. 3. Decouple logic from presentation Build one robust AI engine that powers a chat widget, a browser extension or a simple web view. When someone asks for a new capability, ask “What decision are you making?” and “Where do you need to make it?” You avoid duplicate work and can adapt fast to new platforms. 4. Capture data as part of the flow The best way to train your model is to let users work as usual. If your AI suggests optimal campaign parameters, log every tweak automatically. Don’t make marketers export logs or fill out extra forms—that creates gaps and biases your training set. 5. Earn trust through real-time dialogue In a conversational UI, let the AI ask clarifying questions (“I see you’re about to launch the summer campaign—should we include last quarter’s top keywords?”) and explain its suggestions inline (“These three segments drove 18% more conversions last month”). Then package the output in a ready-to-send summary or email draft. 6. Shift from one-off tasks to continuous value If your tool only fires during project kick-off, users will forget it. Surface a lightweight insight each week—like an alert when support ticket volume spikes or when a key metric drifts. Those small, correct nudges build confidence and prime users for the big recommendations they’ll need later. Validate your assumptions about channel, data capture, trust and engagement before you write a line of production code. When your AI lives inside the tools people already use, it becomes part of their daily routine—and that’s when it becomes indispensable. The Big Takeaway: AI-first products must be invisible, conversational, and proactive, living inside users’ existing tools. Don’t build a standalone app for control—tackle the engineering to embed your AI where it belongs. That’s how you build a platform, not a feature.

"It works on my machine." A dev shared this horror story at a conference last month. Until it didn't work on 50k other machines. They had built a customer onboarding app using an AI app builder and it looked perfect in development. Tested it with their account. Smooth as butta. Tested it with a test account. Flawless. Deployed to production. Disaster. Over 50,000 users tried to sign up on launch day. 49,847 got error messages. The AI had hardcoded their specific user ID into the authentication flow. It worked for them because they were user #1. Everyone else got: "Authentication failed. Please try again." Their testing process was garbage: ❌ Test with my own account ❌ Test with one dummy account ❌ Deploy and pray Here's what I learned about testing AI-generated code: 1. Test with multiple user types: New users Existing users Admin users Users with weird data 2. Test edge cases: Very long names Special characters Empty fields Maximum values 3. Load test everything: 10 simultaneous users 100 simultaneous users What breaks first? 4. Test the whole user journey: Fresh browser Different devices Different networks AI writes code that works for the examples you give it. If you only show it perfect scenarios, it only handles perfect scenarios. p.s. if you want AI that generates code tested for real-world scenarios, Empromptu.ai includes built-in tools that catch these issues before deployment.

"Quality starts before code exists", This is how AI can be used to reimagine the Testing workflow Most teams start testing after the build. But using AI, we can start it in design phase Stage - 1: WHAT: Interactions, font-size, contrast, accessibility checks etc. can be validated using GPT-4o / Claude / Gemini (LLM design review prompts) - WAVE (accessibility validation) How we use them: Design files → exported automatically → checked by accessibility scanners → run through LLM agents to evaluate interaction states, spacing, labels, copy clarity, and UX risks. Stage - 2: Tools: • LLMs (GPT-4o / Claude 3.5 Sonnet) for requirement parsing • Figma API + OCR/vision models for flow extraction • GitHub Copilot for converting scenarios to code skeletons • TestRail / Zephyr for structured test storage How we use them: PRDs + user stories + Figma flows → AI generates: ✔ functional tests ✔ negative tests ✔ boundary cases ✔ data permutations SDETs then refine domain logic instead of writing from scratch. Stage - 3: Tools: • SonarQube + Semgrep (static checks) • LLM test reviewers (custom prompt agents) • GitHub PR integration How we use them: Every test case or automation file passes through: SonarQube: static rule checks LLM quality gate that flags: - missing assertions - incomplete edge coverage - ambiguous expected outcomes - inconsistent naming or structure We focus on strategy -> AI handles structural review. Stage - 4: Tools: • Playwright, WebDriver + REST Assured • GitHub Copilot for scaffold generation • OpenAPI/Swagger + AI for API test generation How we use them: Engineers describe intent → Copilot generates: ✔ Page objects / fixtures ✔ API client definitions ✔ Custom commands ✔ Assertion scaffolding SDETs optimise logic instead of writing boilerplate. THE RESULT - Test design time reduced 60% - Visual regressions detected with near-pixel accuracy - Review overhead for SDETs significantly reduced - AI hasn’t replaced SDETs. It removed mechanical work so humans can focus on: • investigation • creativity • user empathy • product risk understanding -x-x- Learn & Implement the fundamentals required to become a Full Stack SDET in 2026: https://lnkd.in/gcFkyxaK #japneetsachdeva

"AI writes code faster. Your job is still to prove it works." My latest free write-up: https://lnkd.in/gkhzcfiR ✍ is all about code review. Over 30% of senior developers now ship mostly AI-generated code. The problem? AI excels at drafting features but stumbles on logic, security, and edge cases - with errors 75% more common in logic alone. The bottleneck has moved from writing code to proving it works. What's changing: Solo devs ship at "inference speed," treating AI like a powerful intern - but the smart ones have built verification systems (high test coverage, manual testing) that catch issues before production. Skip review and you don't eliminate work, you defer it. Teams face a different challenge: AI floods volume. PRs are ~18% larger, incidents per PR up ~24%, change failure rates up ~30%. When output increases faster than verification capacity, review becomes the rate limiter. Security remains non-negotiable for human oversight. ~45% of AI-generated code contains security flaws. Logic errors at 1.75× the rate of human code. XSS vulnerabilities at 2.74×. The emerging best practice? A simple PR contract: → What/why in 1-2 sentences → Proof it works (tests, screenshots, logs) → Risk tier + which parts were AI-generated → Where you need human input If you can't fill this out, you don't understand your own change well enough to ask someone to approve it. Proof over vibes. The human is ultimately responsible for what the AI delivers. #ai #programming #softwareengineering

Most software engineers think of testing as ensuring the code runs as expected. With AI? That’s only the beginning. AI isn’t just executing predefined instructions—it’s making decisions that impact real lives. In industries like healthcare, law enforcement, and finance, an AI system that “works” in a test environment can still fail catastrophically in the real world. Take Microsoft’s Tay chatbot from years ago as an example.  It wasn’t broken in a traditional sense—it just wasn’t tested against adversarial human behavior.  Within hours, it spiraled out of control, generating offensive content because the testing process didn’t account for real-world unpredictability. This is where traditional software testing falls short. ✔️ Unit testing ensures individual components function. ✔️ Integration testing checks if modules work together. ✔️ Performance testing evaluates speed & scalability. ✔️ Regression testing re-runs test cases on recent changes. But for AI, these checks aren’t enough. AI needs additional layers of validation: 🔹 Offline testing – Does the model work across multiple test cases and adapt to new data? 🔹 Edge case evaluation – Does it handle unexpected or adversarial inputs? 🔹 Scalability assessment – Can it maintain accuracy with growing datasets? 🔹 Bias & fairness testing – Does it make ethical decisions across groups? 🔹 Explainability checks – Can you understand how it reached a decision? (Critical in specific applications.) 🔹 Post-deployment testing – Can it maintain accuracy after deployment? I’ve seen companies launch AI tools in a matter of weeks—only to shut them down a few months later due to complaints or embarrassing failures—all due to a lack of AI testing. If your AI tool passes software functionality checks but fails on quality, scalability, and adaptability, it's time to peel back the layers. AI tools shouldn't just “run.” They need to work reliably in the real world over prolonged periods of time.

How do we think about testing the LLM output portions of a generative AI driven chatbot? How do we break that testing down? What problems come up, what challenges do we face? I had to think about this question recently. I decided to capture some of my thinking in a cartoon. Our approach will change based on context and implementation specifics, but we can imagine a generic chatbot implementation. There is usually a wrapper application that combines user input with system context to create input fed to the AI model. The output will be instructions from the AI model (via interaction supported by the chatbot app or whatever AI framework is assisting it) to tools extending system capabilities, and ultimately the response given back to the user. A further input comes from responses from tools back into the context given to the model. The combined context created by the chat app from the user request and the system context contains instructions and functional points which are to guide the LLM output. Each of those functional points describe something of interest to test. For example, if the system context contains "Only answer questions about return policies, all other questions should be answered with 'I am only able to answer questions about return policies. May I help you with something else?'", then that becomes a function point to test. The problem then consists of at least the following - scaling up the creation of interesting user requests - appropriate oracles and mechanisms for checking calls to tools - appropriate oracles and methodology for checking response output at scale There are more problems beyond those three, both within the LLM output testing as well as testing the larger system. Those three are daunting enough because LLMs allow and produce unstructured data with wide variance whose correctness is highly subjective. We wind up looking for solutions robustly guide us toward identifying problems, bugs, issues that cover a large volume of inputs and outputs. Typical techniques for reducing a test input domain smartly have to be adjusted. There are still boundaries and edges that matter, but they are defined in ways more rough, more nuanced by statistics that crisp splits in the data domains. Breaking our problem down first begins our search for the solutions. #softwaretesting #softwaredevelopment You can find more of my articles and cartoons in my book Drawn to Testing, available in Kindle and paperback format. https://lnkd.in/gM6fc7Zi