Innovate QA Meetup Group

Virtual: Governing AI, Leading Quality

Wed, Oct 7 · 4:00 PM PDT

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Online

Online

Speaker: Aditi Agrawal | LinkedIn

Topic: Governing AI, Leading Quality

Agenda:
🕛 4:00–4:05 — Welcome
🕛 4:05–4:55 — Aditi Agrawal | LinkedIn - Governing AI, Leading Quality
🕛 4:55–5:00 — Closing

Summary:
* A practical view of AI governance in QE — where governance fits across the SDLC and why it matters beyond compliance.
* A framework for governing AI-enabled testing and agents — including human oversight, access, approval, and accountability.
* Ways to balance speed with control so AI improves productivity without increasing quality or business risk.
* Metrics that matter — how to measure AI value alongside reliability, risk, traceability, and control effectiveness.

🎙️ Speaker Opportunities at Our Meetups

Have expertise in QA, leadership, or tech skills you'd love to share? We're always looking for engaging speakers for our monthly meetups. This is a fantastic platform to showcase your knowledge, connect with the community, and inspire others. If you're interested submit your topic here —we’d love to hear from you!

AI Governance Needs a Test Plan: Turning Principles into Evidence

Thu, Oct 15 · 4:00 PM PDT

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Online

Online

Speaker: Tanvi Mittal | LinkedIn

Topic: AI Governance Needs a Test Plan: Turning Principles into Evidence

Agenda:
🕛 4:00–4:05 PM PST — Welcome
🕛 4:05–4:55 PM PST — Tanvi Mittal | LinkedIn - AI Governance Needs a Test Plan: Turning Principles into Evidence
🕛 4:55–5:00 PM PST — Q&A & Closing

Summary :
AI governance is increasingly shaped by frameworks such as the EU AI Act, the NIST AI Risk Management Framework, and ISO/IEC 42001. These frameworks define what AI systems need to demonstrate around safety, fairness, transparency, accountability, and risk management, but they often leave an important question unanswered: how do you actually prove it?

In this session, Tanvi Mittal will connect AI governance principles to practical Quality Engineering and testing practices. The discussion will explore how requirements around risk management, robustness, human oversight, and continuous monitoring can be translated into concrete QA artifacts and evidence.

Tanvi will introduce PROBE — Probabilistic Baseline, Red Team It, Observe in Production, Behavioral Contracts, and Evals Not Assertions — and show how each part can be applied to testing non-deterministic AI systems.

The session will also explore how QA teams can create practical evidence for AI behavior, including distributions, adversarial test suites, production monitoring, behavioral contracts, and evaluation gates.

Takeaways :
What attendees will learn:

1. How to translate AI governance principles into practical QA and testing activities
2. How probabilistic baselines can help establish expected AI system behavior
3. How to use red-team testing to identify risks and weaknesses in AI systems
4. How production observation and continuous monitoring can provide evidence of AI behavior
5. How behavioral contracts can turn governance expectations into testable requirements
6. How evaluation gates can be used to assess non-deterministic AI systems beyond traditional assertions
7. How QA teams can provide evidence of AI system behavior to auditors and executives

🎙️ Speaker Opportunities at Our Meetups

Have expertise in QA, leadership, or tech skills you'd love to share? We're always looking for engaging speakers for our monthly meetups. This is a fantastic platform to showcase your knowledge, connect with the community, and inspire others. If you're interested submit your topic here — we’d love to hear from you!

How to evaluate or test the product powered by large language models

Tue, Oct 20 · 4:00 PM PDT

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Online

Online

Speaker: Narendra Singh Panwar | LinkedIn

Topic: How to Evaluate or Test Products Powered by Large Language Models

Agenda:
🕛 4:00–4:05 PM PDT — Welcome
🕛 4:05–4:55 PM PDT — Narendra Singh Panwar | LinkedIn - How to Evaluate or Test Products Powered by Large Language Models
🕛 4:55–5:00 PM PDT — Q&A & Closing

Summary :
LLMs and AI agents are powering the next generation of products. While the speed of development has increased significantly, evaluating the end-to-end quality of these products remains a challenge.

In this session, Narendra Singh Panwar will share practical learnings and techniques from testing consumer-facing products powered by LLMs and AI agents. The discussion will explore how traditional QA practices need to evolve when testing systems that are non-deterministic and capable of making decisions, generating responses, and completing actions.

The session will cover how teams can evaluate the non-deterministic nature of AI systems, verify factual accuracy, and determine where AI-assisted evaluation can help. Narendra will also discuss the use of LLM-as-a-Judge and where human evaluation is still necessary to assess the quality of AI-powered experiences.

The discussion will also look at AI-powered test automation and continuous customer experience monitoring as ways to support quality evaluation as AI products evolve.

Takeaways :
What attendees will learn:

1. How to evaluate the non-deterministic behavior of LLM-powered products
2. Approaches for fact-checking and evaluating AI-generated responses
3. Where AI and LLM-as-a-Judge can be applied in quality evaluation
4. Where human evaluation is still necessary
5. How AI-powered test automation can support testing of AI products
6. How continuous CX monitoring can help evaluate end-to-end quality
7. How traditional QA practices can evolve for products powered by LLMs and AI agents

🎙️ Speaker Opportunities at Our Meetups

Have expertise in QA, leadership, or tech skills you'd love to share? We're always looking for engaging speakers for our monthly meetups. This is a fantastic platform to showcase your knowledge, connect with the community, and inspire others. If you're interested submit your topic here — we’d love to hear from you!

Accelerating Playwright Migration with AI and Automated Visibility

Wed, Nov 4 · 9:00 AM PST

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Online

Online

Speaker: Divya Prakash | LinkedIn

Topic: Modernizing the Monolith: Accelerating Playwright Migration with AI and Automated Visibility

Agenda:
🕛 9:00–9:05 AM PST — Welcome
🕛 9:05–9:55 AM PST — Divya Prakash | LinkedIn Modernizing the Monolith: Accelerating Playwright Migration with AI and Automated Visibility
🕛 9:55–10:00 AM PST — Q&A & Closing

Summary :
Modern Quality Engineering requires a layered approach that distributes testing across API, UI, and production rather than relying on a single layer to catch every issue.

In this session, Divya Prakash will share a practical approach to building a modern Quality Engineering strategy, starting with an API-first foundation to catch defects early in core business logic. The discussion will explore how teams can strengthen their testing strategy while improving speed, stability, and reliability across the delivery pipeline.

The session will also cover modernizing legacy UI automation by migrating Selenium Java suites to Playwright TypeScript, along with practical ways to streamline automation workflows through automatic test-result uploads and defect creation directly in Jira.

The discussion will then move beyond deployment with shift-right practices that extend testing into live production environments. Divya will demonstrate how Datadog synthetic monitoring can continuously run real-world user journeys in production, helping teams identify issues proactively before customers encounter them.

The session will bring these practices together into one practical blueprint, showing how API testing, UI automation, automated reporting, and production monitoring can work together to create a more reliable and resilient Quality Engineering ecosystem.

Takeaways :
What attendees will learn:

1. How to build a modern, layered Quality Engineering strategy across API, UI, and production
2. Why an API-first approach can help catch defects earlier in core business logic
3. How to modernize legacy Selenium Java automation using Playwright TypeScript
4. How automated Jira reporting can reduce manual effort and improve visibility into test results and defects
5. How shift-right testing extends Quality Engineering into live production environments
6. How synthetic monitoring can continuously validate real-world user journeys and detect production issues proactively
7. How ReadyAPI, Playwright, and Datadog can work together as part of an end-to-end Quality Engineering strategy

🎙️ Speaker Opportunities at Our Meetups

• Have expertise in QA, leadership, or tech skills you'd love to share? We're always looking for engaging speakers for our monthly meetups. This is a fantastic platform to showcase your knowledge, connect with the community, and inspire others. If you're interested submit your topic here —we’d love to hear from you!