GitHub Copilot Mastery: ProTips Beyond the Basics

Buckle up! This session isn’t just about using GitHub Copilot—it’s about mastering it. I’m diving into the power moves, expert techniques, and real-world .NET C# examples that separate casual users from those who truly unlock Copilot’s full potential.

Forget the basics. I’m showcasing fast-paced, high-impact demonstrations of advanced Copilot features, from multi-file editing to live, context-aware code transformations powered by Copilot Edits.

Then I’ll shift gears into something brand new: Copilot’s Agent Mode—a powerful AI upgrade that doesn’t just assist, it acts. Think of it as your coding co-pilot that plans, executes, and iterates on tasks using natural language. Whether it’s suggesting terminal commands or automatically fixing issues across your app, this agentic AI helps you build and ship faster.

Key Highlights:
- Beyond Autocomplete: Experience how Copilot can refactor, optimize, and rewrite code with minimal input, maintaining coding consistency across large projects.
- Precision Prompting: Learn expert-level prompt techniques to fine-tune Copilot’s output, from code refactoring to generating entire functions with context-aware efficiency.
- Debugging at the Speed of AI: Watch Copilot identify and fix bugs in real time, leveraging AI-powered troubleshooting like a seasoned pro.
- Automating the Future: Discover how Copilot integrates with DevOps pipelines, CI/CD automation, and even code reviews, supercharging your workflow.

By the end of this session, you won’t just use Copilot—you’ll command it. Get ready to code smarter, faster, and like an absolute pro.

Building Apps on Autopilot: Meet Your AI Powered Co-Engineer

Imagine an AI co-engineer that doesn’t just suggest code, but plans, builds, tests, and improves your app with you. In this session, I’ll show you exactly that with GitHub Copilot Agent Mode, and then take it further.

Building apps doesn’t stop at your IDE, so we’ll jump into GitHub.com to see how the GitHub Coding Agent tackles real-world backlog items directly from Issues, turning ideas into working features, writing tests, and creating pull requests.

You’ll get a clear look at the architecture that powers these AI agents, hands-on techniques for writing prompts that keep them productive, and a live demo where I use agents to build a small game and complete additional features, all driven by AI. By the end, you’ll know how to put autonomous development to work across your entire workflow, from code to cloud.

Key Takeaways
- See AI take the lead — Watch a Copilot agent plan, code, debug, and refactor an app in your IDE and on GitHub.com.

- Understand the tech behind it — Get a clear view of the architecture (Agent-Computer Interface, Model Context Protocol) and how the Coding Agent integrates into your GitHub workflow.

- Prompt like a pro — Learn how to craft prompts that help your AI co-engineer stay productive without micromanagement.

Test Smarter with AI: Playwright Automation at Scale on Azure

Manual testing slows delivery and limits confidence in release quality. In this session, you'll see how AI bridges the gap by turning manual test plans into automated Playwright scripts using GitHub Copilot’s Model Context Protocol (MCP). Combined with Microsoft’s Azure App Testing Service, we’ll show how to run these tests at scale across browsers and platforms. The result is faster feedback, fewer regressions, and more resilient applications. If your team is ready to reduce testing bottlenecks and embrace intelligent automation, this session is your next step.

Key takeaways:

AI Converts Manual to Automated: Use GitHub Copilot with MCP to turn plain English test cases into real Playwright scripts—faster, smarter, and with less rework.

CI/CD-Ready from the Start: Integrate automated tests directly into your pipelines and Azure Test Plans for full traceability, reporting, and continuous validation.

Test at Scale with Azure App Testing: Run UI tests in parallel across browsers and platforms using Azure’s high-scale test infrastructure—no local setup needed.

AI Powered IoT: Turning Real Time Data Into Smarter, Faster Business Decisions

Step into the next generation of IoT, where AI amplifies everything from device telemetry to business outcomes. In this session, you will see how AI enhanced Azure services can turn raw device data into immediate, actionable intelligence with minimal code and rapid development cycles. We will build a real-world IoT solution that captures data at the edge, processes it through Azure Functions and Logic Apps, stores it in Cosmos DB, and visualizes it with Azure Maps, all guided by AI-driven insights. If you want to understand how AI and Azure combine to unlock real time decision making across any industry, this session will show you what is possible today.

Key Takeaways

See how AI accelerates IoT workflows across data capture, processing, and visualization.

Learn a fast, practical architecture for building real time IoT solutions on Azure with minimal complexity.

Walk away knowing how AI can turn everyday device data into clear, actionable decisions for your business.

AI Powered Observability: Master Your App Performance with Azure Application Insights

Modern apps move fast, and your visibility needs to move even faster. In this session, you will learn how Azure Application Insights, now backed by AI driven detection and analysis across Azure Monitor, gives you a complete view of your application’s health, performance, and user behavior. We will break down how AI assisted anomaly detection, session replay, distributed tracing, and end-to-end transaction analysis help you uncover issues long before customers notice them. Then we will walk through a live demo showing how Application Insights integrates with your DevOps workflow to deliver continuous, intelligent monitoring that keeps your apps performant, reliable, and ready for scale.

Expect to see how AI is reshaping observability on Azure, giving developers the confidence to ship faster and fix smarter.

Key Takeaways

Understand how AI driven insights in Azure Application Insights surface performance issues, anomalies, and patterns in real time.

Learn how to trace, diagnose, and resolve problems quickly using the latest capabilities like session replay, distributed tracing, and automated anomaly detection.

See how Application Insights fits into a modern DevOps workflow, enabling proactive optimization throughout the build, test, and release cycle.

From prompts to productivity: Lessons learned with GitHub Copilot and agentic AI

Go beyond basic completions and unlock the full potential of GitHub Copilot. In this fast-paced session, you’ll learn practical tips for better prompts, debugging, validation, and code reviews — plus see how Copilot fits across the Recon → Merge workflow. You'll also get a preview of what’s next with agentic AI, where routine tasks like creating branches, writing tests, and opening pull requests start to run under your guidance. Walk away with actionable strategies to boost productivity today and prepare for tomorrow’s AI-driven development.

Learn lessons and tips for maximizing GitHub Copilot beyond simple completions
Learn how to apply GitHub Copilot across the full Recon → Merge workflow
Prepare for the future of agentic AI automating developer tasks under your guidance

(Workshop) Found Means Fixed: Hands-On GitHub Advanced Security

Security vulnerabilities don't fix themselves, but with the right tools and practices, fixing them can become second nature. In this intensive hands-on workshop, you'll master GitHub Advanced Security (GHAS) by actively detecting, preventing, and remediating real security issues in a live codebase. From identifying code quality problems to blocking secrets before they leak, you'll experience the full security lifecycle using the same tools trusted by enterprises worldwide.

Walk away with practical skills in code scanning, dependency analysis, secret protection, and organization-wide security management. Minimal slides, maximum hands-on, just you, your IDE, and a mission to secure code like a pro.

What You'll Accomplish

Master Detection & Prevention
Enable and configure the complete GHAS suite. Experience hands-on vulnerability detection through Code Quality analysis, CodeQL scanning, Dependency Review, and Secret Protection. Use Copilot Autofix to remediate issues, implement repository rulesets to block dangerous code, and watch push protection stop secrets in real-time.

Command Organization-Wide Security
Navigate Security Overview dashboards to visualize risks across your entire organization. Create and manage security campaigns that coordinate remediation efforts across teams. Master secret scanning workflows including token revocation, bypass governance, and custom pattern management at enterprise scale.

Implement Advanced Security Automation
Build custom CodeQL queries tailored to your codebase's unique risks. Configure advanced scanning workflows, integrate third-party security tools, and implement enterprise-grade automation that scales across repositories and teams.

(Workshop) Building Apps on Autopilot: Meet Your AI Powered Co-Engineer

Imagine an AI co-engineer that doesn't just suggest code, it architects solutions, generates entire features, writes comprehensive tests, and debugs alongside you. In this immersive hands-on workshop, you'll experience the full power of GitHub Copilot as it transforms from autocomplete into your autonomous development partner.

We'll build a complete full-stack aviation management system from scratch, guided by your AI co-engineer. Watch as natural language specifications become REST APIs, database schemas, React components, and deployment workflows, all generated through intelligent conversation. You'll master Agent Mode for complex multi-file operations, learn prompt engineering techniques that 10x your productivity, and discover how AI handles everything from debugging crashes to optimizing performance.

Through progressive hands-on labs spanning backend development, database design, testing strategies, DevOps automation, and modern React frontends, you'll learn exactly when to let AI take the lead and when to steer. By the end, you'll have practical skills to ship faster, code smarter, and focus on architecture rather than syntax, transforming yourself from code writer to solution orchestrator.

See AI architect in action. Watch GitHub Copilot Agent Mode plan, implement, test, and refactor a complete full-stack application through progressive hands-on labs, from API endpoints to database schemas to deployment workflows.

Master the AI development workflow. Learn proven prompt engineering patterns, understand when to use Chat vs Agent Mode vs inline suggestions, and discover techniques that accelerate development velocity by 10x.

Ship production-ready code faster. Walk away with practical skills in AI-assisted testing, debugging, refactoring, documentation, and DevOps, plus a complete aviation app you built together with your AI co-engineer.

The AI Test Engineer: Creating Smarter Tests from UI to Load

Testing teams are under pressure to move faster, improve coverage, create better test data, and validate quality earlier in the delivery lifecycle. AI can help turn that pressure into practical progress.

In this session, we will explore how GitHub Copilot and modern engineering practices can help quality teams create meaningful test cases, generate realistic test data, write Playwright tests, create JMeter scripts, and automate Azure Load Testing through GitHub Actions. Along the way, we will build a functional load testing workflow from the ground up, connecting the pieces needed to run tests as part of a repeatable delivery process.

The session connects real testing scenarios with core concepts from the Azure Load Testing and GitHub Actions learning path, including workflow automation, secrets, service authentication, JMeter-based load tests, YAML configuration, fail criteria, auto-stop rules, and pipeline-based performance validation.

Attendees will see how AI can act as a practical testing partner, helping teams move from manual testing ideas and scattered scripts to automated, cloud-ready quality checks. The focus is not just generating test code, but using AI to build a stronger testing workflow, from functional validation to performance testing to CI/CD automation.

Key Takeaways
- Use AI to create stronger test coverage. Learn how AI can help generate test cases, identify edge cases, create test data, and turn requirements into practical testing scenarios.

- Create modern UI and performance tests faster. See how AI can help write Playwright tests for browser automation and JMeter scripts for load and performance testing.

- Build load testing into the delivery pipeline. Learn how GitHub Actions, Azure authentication, YAML configuration, fail criteria, and auto-stop rules come together to deploy and run a functional load testing workflow.

Visual Studio Superpowers: Hidden Productivity Tricks and GitHub Copilot Secrets

Most developers live in Visual Studio every day, but even experienced users often miss the features that can save the most time. This session is a fast-paced, demo-driven tour of Visual Studio productivity superpowers that are easy to overlook, hard to forget, and immediately useful.

You'll learn practical techniques for moving through code faster, editing with less friction, refactoring safely, debugging smarter, and streamlining everyday development tasks. Along the way, we'll uncover hidden IDE features, keyboard-driven workflows, search and navigation tricks, debugging shortcuts, and small habits that eliminate repeated clicks from your day.

Then we'll bring GitHub Copilot into the workflow, not as a replacement for developer skill, but as a force multiplier inside Visual Studio. You'll see how Copilot can help explain unfamiliar code, generate tests, improve documentation, assist with debugging, modernize older code, and accelerate repetitive development tasks.

This is not a "what's new" tour or a slide-heavy feature dump. It's a practical field guide for developers who want to get more out of the tools they already use every day. Whether you've been using Visual Studio for months or decades, you'll leave with practical tips and techniques you can start using immediately.

Takeaways
- Discover overlooked Visual Studio features that help you move, edit, refactor, and debug faster.
- Learn where GitHub Copilot fits naturally into real Visual Studio development workflows.
- Leave with practical productivity habits you can apply the next time you open your IDE.

The AI Wrote It. Now Prove It: Safe Refactors, Playwright, and Copilot Code Review

AI can produce code faster than most teams can safely review it. That changes the bottleneck. The hard part is no longer getting a change written, it is proving the change is correct, safe, and ready to ship.

In this fast-paced, demo-driven session, I’ll take one legacy workflow through a practical safety pipeline. We’ll capture its current behavior, create characterization tests, and use GitHub Copilot to make small, targeted refactors instead of one risky rewrite. After each change, we’ll compile, test, inspect the diff, and create a rollback-friendly checkpoint.

Then we’ll move beyond code-level tests. You’ll see GitHub Copilot and Playwright exercise the real user journey, capture traces and screenshots, and uncover regressions that unit tests can miss. We’ll finish with a GitHub Copilot-assisted pull request review that challenges the change for defects, security risks, edge cases, and missing coverage.

This is not a session about trusting AI-generated code. It is a repeatable way to demand evidence from it, while keeping a human responsible for the final release decision.

Takeaways
- Constrain AI changes to small, reviewable diffs.
- Prove behavior with tests and Playwright.
- Keep humans responsible for the final merge.

One Slice, Zero Big Bang: Modernize WPF with Blazor and GitHub Copilot

Legacy WPF applications often contain years of valuable business logic, hidden rules, and workflows that cannot simply be rewritten over a weekend. The safer path is to modernize one valuable slice at a time while the application continues to serve the business.

In this demo-driven session, I’ll show how to select a low-risk workflow seam, define what behavior must remain unchanged, and introduce Blazor into an existing WPF application through `BlazorWebView`. I’ll replace one legacy screen while keeping the rest of the desktop application running around it.

GitHub Copilot will help assess the existing code, uncover dependencies, scaffold the Blazor component, connect shared services, and generate tests. We’ll compare the old and new workflows, verify behavioral parity, and use clear rollback boundaries so the modernized slice can be reversed without disrupting the rest of the application.

You’ll leave with a practical modernization pattern that delivers visible progress without a big-bang rewrite, a frozen feature backlog, or years of waiting for the new system to arrive.

Takeaways
- Select the safest workflow seam to modernize first.
- Run WPF and Blazor together during migration.
- Preserve behavior with parity checks and rollback paths.

From Wireframe to Working Feature: Stories, Blazor, Tests, and GitHub Copilot

A wireframe may look like a simple picture, but it hides dozens of decisions about behavior, validation, data, APIs, error states, and user expectations. Asking AI to “build this screen” skips those decisions and usually produces a convincing interface with an incomplete specification underneath it.

In this fast-paced session, I’ll start with only a wireframe image and use GitHub Copilot to uncover the specification hidden inside it. We’ll turn visual elements into user stories, generate testable acceptance criteria, identify assumptions, and surface unanswered questions before writing code.

Next, we’ll map each control to domain logic and typed API contracts, then scaffold the feature as a set of focused Blazor components. Copilot will help generate the implementation and tests, but we’ll review every decision, resolve ambiguity, and preserve existing business rules where the wireframe represents a modernized legacy workflow.

Finally, we’ll validate the finished feature against the original wireframe and acceptance criteria. You’ll see a repeatable path from an ambiguous visual artifact to working, reviewable, and testable software.

Takeaways
- Turn wireframes into testable requirements.
- Map UI controls to domain logic and APIs.
- Validate the feature against the original design.

Beyond Prompting: Build a Team-Ready GitHub Copilot System Without Losing Control or Blowing the Bud

Most developers use GitHub Copilot one conversation at a time. A useful prompt works once, stays on someone’s laptop, and disappears when the chat ends. That may help an individual, but it does not create a repeatable engineering practice for the team.

In this demo-driven session, I’ll take one valuable development workflow and progressively turn it into a team-ready GitHub Copilot system. We’ll begin with focused context and a well-defined prompt, then promote the workflow into repository instructions, a reusable prompt file, a packaged skill, and a specialized agent with clear boundaries and stop conditions.

When the workflow needs trusted information beyond the repository, we’ll connect it to a narrowly scoped, read-only MCP tool. Along the way, we’ll decide when each layer earns its place, how to preserve human approval points, and how to keep these assets versioned and reviewable like code.

We’ll also examine the cost side of AI-assisted development. You’ll see how bloated context, long conversations, unnecessary model changes, and oversized requests affect both answer quality and AI-credit consumption. We’ll use focused working sets, deliberate model selection, fresh-start handoffs, budgets, and usage telemetry to keep the system useful without making cost invisible.

The goal is not to build the most complicated GitHub Copilot setup. It is to choose the lightest approach that makes important work repeatable, governed, measurable, and affordable.

Takeaways
- Choose the lightest GitHub Copilot customization for each task.
- Control quality and cost with focused context.
- Add agents and MCP only with clear guardrails.

Stop Paying for Bad Context: Make Copilot Faster, Cheaper, and More Accurate

With usage-based AI, poor context no longer wastes only time. It can also consume credits while producing weaker answers.

In this rapid-fire session, I’ll show how oversized prompts, entire-repository context, long-running conversations, and unnecessary model changes quietly increase cost and reduce accuracy. You’ll see practical habits for narrowing the working set, defining what “done” looks like, matching the model to the task, and recognizing when a conversation has become too stale to save.

Better results do not always require a more powerful model or a longer prompt. They often begin by removing everything the model does not need.

Takeaways
- Remove irrelevant context before every request.
- Match model cost to task value.
- Reset stale conversations before they drift.

Autonomy Is a Dial, Not a Switch: Give Copilot More Freedom Without Giving Up Control

Teams often treat AI autonomy as an all-or-nothing choice. Either Copilot suggests a few lines, or an agent receives permission to edit files, run commands, and make decisions across the project. Neither extreme fits every task.

In this focused session, I’ll introduce a simple autonomy dial based on blast radius, reversibility, and risk. We’ll turn it up for scaffolding, boilerplate, and repetitive changes, then dial it back for security, authentication, financial logic, and unclear requirements.

You’ll see how smaller scopes, explicit stop conditions, validation checkpoints, and human approval let teams gain speed without handing critical decisions to the model.

Takeaways
- Match autonomy to the task’s blast radius.
- Use checkpoints for critical code paths.
- Keep humans responsible for approval and merge.

Modernize Without Breaking It: AI-Powered Playwright Testing at Scale on Azure

Modernizing an application is exciting until a refactor quietly breaks a critical user journey. Unit tests can protect individual methods and services, but they cannot always prove that the complete application still works the way users expect.

In this demo-driven session, you’ll see how GitHub Copilot and the Playwright Model Context Protocol help turn manual test plans and plain-language scenarios into resilient browser automation. I’ll use Playwright to validate real user journeys, strengthen assertions, reduce flaky tests with reliable locators and intelligent waits, and capture traces, screenshots, and logs when something fails.

Then I’ll move those tests into CI/CD and Azure Test Plans, where they become repeatable release gates with clear reporting and traceability. Finally, you’ll see how Azure App Testing runs Playwright tests in parallel across browsers, platforms, and environments without requiring your team to maintain its own browser infrastructure.

The result is more than faster test creation. It is a practical safety net for modernization, giving teams quicker feedback, fewer regressions, and stronger evidence that the modernized experience still works before it reaches production.

Takeaways
- Turn scenarios into automation: Use GitHub Copilot and MCP to generate reviewable Playwright tests from manual test cases.
- Make tests release-ready: Integrate Playwright with CI/CD and Azure Test Plans for continuous validation and traceability.
- Scale without a browser farm: Run tests in parallel across browsers and platforms with Azure App Testing

(FastFocus) Visual Studio Superpowers: Hidden Productivity Tricks and GitHub Copilot Secrets

Most developers use Visual Studio every day, but even experienced users miss features that can save valuable time. In this fast-paced, demo-intensive session, I’ll showcase hidden productivity tricks for navigating code, editing faster, refactoring safely, debugging smarter, and eliminating unnecessary clicks.

You’ll also see practical GitHub Copilot techniques for understanding unfamiliar code, generating tests, diagnosing problems, and accelerating repetitive tasks directly inside Visual Studio.

This is not a slide-heavy feature tour. It’s 20 minutes of rapid-fire demonstrations and immediately useful techniques you can put to work the next time you open Visual Studio.

Takeaways
- Discover overlooked Visual Studio features that help you move, edit, refactor, and debug faster.
- Learn where GitHub Copilot fits naturally into real Visual Studio development workflows.
- Leave with practical productivity habits you can apply the next time you open your IDE.

AI-Powered Observability: Find and Fix Production Issues with Azure Application Insights

Production problems rarely arrive with useful details. Customers report that checkout is slow, an order failed, or something simply did not work. Developers must determine whether the problem lives in the browser, API, database, a dependency, or the latest code change.

In this demo-driven session, you’ll see how Azure Application Insights helps developers follow real customer journeys across a modern React, ASP.NET Core, and Azure SQL application. We’ll use Playwright to generate realistic traffic, then investigate slow requests, API failures, browser exceptions, dependency bottlenecks, and suspicious runtime behavior.

You’ll see how distributed tracing, transaction analysis, custom telemetry, KQL, alerts, and release correlation turn vague symptoms into actionable evidence. Finally, we’ll bring those findings back to the repository and use GitHub Copilot to understand the affected code, identify likely causes, and propose a safe fix.

This is observability for application developers, focused on finding the problem, fixing the right code, and verifying the customer experience.

Takeaways
Trace production problems across the browser, API, and database.
Use telemetry to isolate failures and performance hotspots.
Turn production evidence into safer fixes with GitHub Copilot.

AI-Powered Observability: Find the Failure Fast with Azure Application Insights

Gino’s Gelato has launched online ordering, but customers are reporting that checkout is slow. Is the problem in the browser, API, application code, or database?

In this rapid, demo-intensive session, we’ll use a Playwright customer journey to reproduce the problem, then follow the transaction through Azure Application Insights from the React frontend to the ASP.NET Core API and Azure SQL. You’ll see how distributed tracing and dependency telemetry turn a vague complaint into a specific performance bottleneck.

Finally, we’ll bring that production evidence back to the repository and use GitHub Copilot to investigate the affected code and identify a safe path forward. No dashboard tour, just one real issue traced from customer symptom to likely code fix.

FastFocus - 20 Min
Takeaways
Reproduce production symptoms with Playwright.
Trace slow transactions across every application layer.
Use telemetry and Copilot to investigate the right code.

The Power of DevOps in the Real World

In the realm of software development, the year is 2025, where good DevOps practices have transformed from mere aspirations to absolute necessities. Continuous value delivery to our end users has become paramount, yet mastering this art is no small feat. Join this epic session on Real World DevOps, where we delve into the "why's" and "how's" of DevOps. Uncover its importance and our unwavering commitment to excellence. Witness a real-world marvel as we forge a complex, modern application from pure source code—no infrastructure exists—ultimately deploying it flawlessly to the cloud, including continuous testing. Explore web front ends, APIs, mobile apps, and beyond, harmonizing all modern technologies. But this is no mere showmanship. Prepare to immerse yourself in advanced DevOps techniques and best practices, prioritizing developer security, infrastructure as code, and fostering collaboration. Experience the transformative power of GitHub Actions as we push the boundaries of the DevOps workflow. Through cutting-edge tooling, we'll unveil a breathtaking demonstration, defying limits and propelling you into an unforgettable journey through the future of software development.

Key Takeaways:
• Understanding the importance of DevOps in modern software development.
• Learning practical application of DevOps techniques through a real-world demonstration.
• Exploring innovation and cutting-edge tooling in the DevOps workflow, with a focus on GitHub Actions.

Mission Copilot Autofix: Securing the World’s Code with AI and GitHub Advanced Security

In today’s fast-paced development cycles, developers aren’t just writing code — they’re also making sure it’s safe. This session explores GitHub Advanced Security (GHAS) and how it empowers developer-first security by weaving automated security checks directly into your GitHub workflow. You’ll see how GitHub’s latest features — including autofix suggestions, secret scanning push protection, and CodeQL-powered code scanning — help teams catch and resolve vulnerabilities early, without disrupting velocity. We’ll also look at how new capabilities like Security Campaigns make it easy to create and track PRs across repositories so you can batch-resolve alerts at scale and monitor progress from a central dashboard. Whether you’re securing one repo or an entire enterprise, you’ll leave with proven GitHub-native strategies for “shifting security left” with confidence.

Key Takeaways:

- Spot and resolve issues faster with AI-powered tools like code scanning autofix, secret scanning, and dependency insight.

- See how GHAS integrates directly into your repos for frictionless, developer-friendly security without extra configuration.

- Learn how to scale security across teams and repositories using built-in visibility, metrics, and Security Campaigns to coordinate large-scale remediation efforts efficiently.

Building Tomorrow’s Apps: AI Powered Development with Azure, OpenAI, and GitHub

Step into the future of app development, where AI meets the cloud to transform your projects from concept to reality. This session will bring you up to speed with Azure OpenAI, SQL Server, and App Service, creating an intelligent Retrieval-Augmented Generation (RAG) application. Discover how GitHub’s Advanced Security and Copilot work in unison to boost your productivity while keeping your code secure. Through real-world examples and practical exercises with the LangChain framework, you'll learn the ins and outs of building scalable, AI-enhanced applications. Walk away with the knowledge and tools to redefine app development with cutting-edge AI integration.

Key Takeaways

Learn to combine Azure OpenAI, SQL Server, and App Services to create a powerful AI-driven RAG application.

Discover how GitHub Advanced Security and Copilot streamline workflows and safeguard your codebase.

Gain practical skills with LangChain and VS Code to build and deploy scalable AI applications.