Spencer at Aviron Software

Building Real World AI-Enabled Features Into Your .NET Apps

Spencer Schneidenbach

President/CTO at Aviron Labs

In order to be an effective AI engineer, you need to know the primitives - and we'll break them all down for you.

Learn the basic constructs of LLMs and why they're important

You'll learn the relevant code packages used in .NET to ship real-world apps integrated with your favorite LLMs.

Understand the code used to build AI-enabled apps in .NET

Looking for more than just the basics? We'll offer some real-world guidance on how we build AI-enabled apps at Aviron.

Get some real-world insights into how AI and .NET integrate

Build AI-powered software reliably & from first principles. Learn the GenAI software development lifecycle: agents, evals, iteration & more

AI Builder/Educator (6 Million Students)

Hugo Bowne-Anderson

AI & data engineer, consultant, educator of 6+ million students (ex-Yale)

AI Builder with 15 years of experience

Stefan Krawczyk

Agentforce; Ex-Stitch Fix; 13+ Years building / productionizing data, ML, Agents

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If you’re a software engineer or data scientist, chances are you’ve built an impressive proof of concept (POC) that showcases the potential of LLMs.But these POCs often fail to scale into reliable, production-ready applications. The result? Endless iteration cycles, unreliable outputs, and frustration as teams struggle in what we call “POC purgatory.”It doesn’t have to be this way.With the right principles, processes, &amp; workflows, you can build LLM applications that work in production, without overcomplicating pipelines or chasing trends. This course focuses on the iterative development, evaluation, and debugging practices that make AI systems production-ready.You'll learn how to build with state-of-the-art tools like RAG, fine-tuning, MCP, context enginerring, and agents, but the emphasis is on building scalable, robust systems, ensuring you leave with the mindset and skills to adapt as the technology evolves.You’ll not only learn how to design, test, &amp; deploy generative AI applications, but also how to iterate and improve rapidly. Join 300+ engineers from Meta, Netflix, Amazon, and more to create AI systems that solve real problems, deliver real value, &amp; scale with confidence.

Master the patterns and processes that underpin successful GenAI app development

Move beyond POCs and build production-ready AI systems

Build State-Of-The-Art AI Agents

Accelerate your development process with best-in-class "Evals" & LLMs Judges

Develop hands-on expertise by building GenAI and LLM apps

Showcase Tangible Results

Data scientists, machine learning engineers who are sick &amp; tired of seeing and building prototypes &amp; want to ship reliable LLM applications

Software engineers who want to learn how to build Generative AI systems and learn the LLM software development lifecycle.

We show you what's possible with AI, then take you there.

Founder | Meta/Amazon/Statsig/Tencent

Statsig

Amazon

Boston Consulting Group (BCG)

Tencent Games

Yuzheng Sun, PhD

Prev. Evangelist @ Statsig (Acq. by OpenAI), AI Director @ Tencent; Meta/Amazon

Applied Scientist | yage.ai

Yan Wang, PhD

Applied Scientist @ Samsara | Olympic Torchbearer | Ex Pinterest/Microsoft | Ex Tech blogger with 100k+ subscribers

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Is this you?<ul><li>Chatting endlessly with an “AI intern” instead of designing the context with clear principles.</li><li>Blaming “hallucinations” when the real issue is poor orchestration.</li><li>Trying to run an entire project from a single, overloaded chat window.</li><li>Treating AI like witchcraft and prompt spells instead of engineering.</li></ul>AI gets better every day. But when it’s time to ship something serious, you don’t yet have a reliable way to make AI show up for you.The problem isn’t AI. It’s how you’re using it. This course exists for that exact gap.AI is a new computiung paradigm. To use it well, you have to:<ul><li>Unlearn old patterns</li><li>Install new mindsets and habits</li><li>Build a meta-learning system that can carry your career for the next 20 years</li></ul>That's hard, and we take it seriously. This course is designed to teach you how to fish. On top of lessons and projects, we built two unique tools:<ol><li><a target="_blank" rel="noopener noreferrer nofollow" href="https://youtu.be/dXjcy6XSN_Y">A build platform</a> that consolidates key APIs and lets you deploy your projects with one command—so you can build fast, not fight boilerplate.</li><li><a target="_blank" rel="noopener noreferrer nofollow" href="https://www.superlinear.academy/c/share-your-projects/">A lifetime community</a> where you can share projects, ask questions, and get unstuck long after the course ends.</li></ol>The end of our course is the beginning of your learning, and we will be with you.

Learn AI for the Next 20 Years, Not the Next 20 Days

Engineer Robust Context to Eliminate Hallucinations

Curate "Sterile" Contexts for Peak AI Performance

Architect Multi-Agent Systems to Conquer Complexity

Define Success Criteria to Transmute from Operator to Manager

Build "White-Box" Evaluation Tools to Visualize Quality

Design Self-Healing Loops for Autonomous Optimization

Software Engineers &amp; Developers Ready to build reliable, scalable AI systems integrated into production workflows.

Technical Product Managers &amp; Strategists Seeking the hands-on knowledge to scope, architect, and guide AI initiatives with confidence.

Data Professionals &amp; Automation Experts Who want to transform proof-of-concept scripts into durable applications that multiply productivity.

Build robust LLM-powered apps, chatbots, and agents. Learn by writing real code, one line at a time.

CEO @ Actualize

Jay Wengrow

Software Engineer and Educator, Author of A Common-Sense Guide to AI Engineering

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Do you have a grasp of basic LLM principles but struggle to put it all into code? Tired of scattered documentation, outdated tutorials, and frameworks that impress in demos but collapse in production?You’re not alone. Building with large language models doesn’t have to be confusing - it can be clear, structured, and deeply rewarding.In this course, you’ll build real LLM-powered apps, one line of code at a time. You'll see how an app is built from scratch in real time, and follow along. Along the way, you'll discover not only how to implement these systems, but the why behind each line of code.From chatbots to agents and RAG to evals, you’ll learn the core concepts of building atop LLMs. More importantly, you’ll get how LLMs “think” - allowing you to guide them to do what you want despite their nondeterministic nature. Additionally, you’ll be able to balance quality, latency, and cost, making big-picture decisions about AI-powered app architecture.AI engineering isn’t just another branch of software development - it’s a different mindset altogether.

Gain hands-on AI Engineering proficiency by writing real code

Gain real-world intuition for working with LLMs

Reliably guide LLMs instead of getting unpredictable results.

Build retrieval-augmented assistants that can access proprietary knowledge

Use evals to systematically optimize your LLM app’s performance over time

Assemble agents that act upon the real world

This course is designed for current software engineers who will use Python  to build LLM-powered apps. We won't teach basic coding here.

Software Engineering

We'll be using Python, but we'll keep it simple in case you're more familiar with other coding languages.

Note about Python

If you want an inside look at what's involved with building AI apps but don't plan on coding yourself, you'll still follow what's going on.

Exception: Product Managers

Software engineers who want to build LLM-powered chatbots and agents, or break into AI engineering more generally

Data Scientists/Engineers who want to build their own LLM-powered apps

Product Managers who want to understand AI engineering from the coding perspective

Build a PM Copilot: AI that remembers everything, plans your day, and reviews your week. No coding required.

Head of Product | AI for non-tech users

Myles Sutholt

Head of Product, 10+ years - Building AI for non-technical healthcare worker

85% of AI users report saving hours every week. (Workday, 2026)When researchers actually measured it, experienced engineers were 19% slower with AI. They believed they were 20% faster. (METR, 2025)If senior engineers cannot tell whether AI is helping them, the rest of us almost certainly cannot either.I saw this in my own teams. Same tools, same AI. They used it like a vending machine: throw in a prompt, some inputs after being asked, get polished output, move on.The problem was not the AI. It was what AI knew about their work. Very little beyond what it asked for.Most CEOs report no net benefit from AI. Only about 1 in 10 capture real gains. (PwC CEO Survey, 2026)I built a system that gives AI my real context that compounds over time: projects, decisions, priorities. It changes the output because it can ask much better questions. It already knows everything it needs to act as a virtual team member. You leave with a working system built on your actual work. Not a demo, not a certificate. From ChatGPT basics to building automations, the course meets you where you are.If you work with projects, decisions, and AI, this is for you.

AI Copilot that remembers everything and plans your week. No coding required.

AI That is Visibly Better

Your AI never forgets who you are or what you're working on

Context-switch between projects without losing anything

Wake up to your priorities already sorted

Your weekly review writes itself every Friday

Enable seamless AI handoffs with teammates

You'll build on AI habits you already have, not learn from scratch.

Use ChatGPT or Claude regularly (not just tried once)

Proves you have the "I'll figure it out" mindset the course requires

Have experience with Notion, Zapier, or similar tools

You'll run terminal commands and see code - AI writes it, but you'll see it.

Willing to try things that feel unfamiliar at first

<pre><code class="language-markdown">You want a competitive edge. Comfortable being a beginner if it means being the person others ask "how did you do that?"</code></pre>

You want a competitive edge. Comfortable being a beginner if it means being the person others ask "how did you do that?"

<pre><code class="language-markdown">You can't work more hours. You need leverage, not tips. You want a system that compounds over time.</code></pre>