Mark Kim-Huang

AI Agents & Multimodal LLMs

Co-Founder & Chief Architect at Gradient

Determine when you should consider using adapted models for table reasoning, pdf parsing or sentiment analysis.

Why and When to Use Task Adapted Models

Use low code APIs to train a base model for a specific task.

Fine-tune LLMs for Task Adaptation

Learn about how task-adapted LLMs can help move your business closer towards deploying autonomous assistants.

How to Shift Towards Agentic Workflows

"Despite being a student in Harvard’s Master’s in Data Science program, I've struggled to understand the concepts that make LLMs so impressive. This class gave me the answers." - Tony "Ishan takes a black box, rips it apart to understandable compartments, and clearly communicates what is going on. Highly recommend." - Joe, CFO ______________________________________________________________________ The current cohort completed recording on January 17th, 2025 but you can still enroll to participate asynchronously: •&nbsp;Get lifetime access to all recorded sessions, including interactive Q&amp;A segments•&nbsp;Connect with peers in our dedicated private Discord community•&nbsp;Master the material through comprehensive quizzes and training resources•&nbsp;Receive direct instructor support via email or Discord whenever you need help•&nbsp;The option to attend a future live cohort for free______________________________________________________________________ Why This Course Stands Out: You already know how vital AI is to your career—it's in every conversation, project, and strategy session. But diving into AI feels daunting with limited time for self-study or complex programming. That’s where this course comes in. Skip the endless prep work and gain an intuitive, hands-on understanding of AI and transformers in just a few hours—without needing an advanced degree. No other course offers such an interactive, fast, and in-depth technical understanding of AI using an actual GPT-2 model, all using an easy-to-learn spreadsheet interface. ______________________________________________________________________ Perfect for Busy STEM Professionals:Whether you're a developer, technical manager, or executive, this course demystifies AI and teaches you exactly how Large Language Models (LLMs) like GPT-2 work, with no prerequisites beyond a STEM background. It’s ideal for: • Software/Web Developers seeking an easy introduction to machine learning. • Science &amp; Tech Professionals navigating AI transformations in their fields. • Technical Sales/Marketing crafting messaging for AI products. • Product/Project Managers overseeing LLM-powered solutions. • Executives/Leaders guiding AI strategy in their organizations. ______________________________________________________________________ What You’ll Learn:Using a unique spreadsheet implementation of GPT-2 (the predecessor to ChatGPT), we’ll break down each model component with crystal-clear explanations. In just a few sessions, you’ll be able to: • Understand how LLMs work—as quickly as possible. • Explain every key component—tokenization, embeddings, attention, and more. • Gain AI literacy—so you can confidently lead and manage AI-driven teams. • Build a foundation for further AI projects—and even more advanced learning. ______________________________________________________________________ Why Spreadsheets?You don’t need to be a coder or a calculus expert to understand AI—just the right approach. This course uses an innovative, no-code, spreadsheet-based method to teach LLMs. It's fast, interactive, and designed for busy professionals like you. By breaking down complex AI principles into easy-to-follow spreadsheet exercises, you’ll gain a lasting, foundational understanding of how large language models work. This is knowledge you can carry across into any model, any programming language, and any project. ______________________________________________________________________ Hands-On Learning, No Programming Required: By the end of the course, you’ll have a working, detailed understanding of how transformers power LLMs like ChatGPT, Claude, and Llama. Plus, you'll have a spreadsheet tool you can explore and tweak to deepen your understanding further. ______________________________________________________________________ See it for Yourself: Still unsure? Watch the FREE lightning lesson:"Inside the Brain of an LLM: How it Works in 30 Minutes" to see how accessible AI learning can be: https://maven.com/p/eeea6f/inside-the-brain-of-an-llm-how-it-works-in-30-minutes ______________________________________________________________________ Join the Community of Learners: • “I could’ve read 20 papers on this and not gotten nearly the understanding I got from this hands-on course.”— Class Participant from Adobe • “Highly recommend this course for anyone wanting to build a foundation in LLMs. Ishan’s interactive approach makes complex concepts incredibly clear.”— Matt, Business Intelligence Analyst ______________________________________________________________________ Practical Details: • Course Access: All sessions are recorded and available to stream after the course concludes, so you’ll never miss a thing.• Reimbursement: Your company may cover this course if you're building or transitioning into AI at work. ______________________________________________________________________ New for this cohort! We've dropped the requirement for Excel. We have a browser based alternative. Some exercises may use Google Sheets and/or Python.

Skip the mountains of prep work and learn how AI works in just a few hours

Course syllabus

If your events are recurring and at the same time, it might be easiest to use a single line item to communicate your course schedule to students

Tuesdays & Thursdays

Feel free to type out dates as your title as a way to communicate information about specific live sessions or other events.

May 7, 2022

Schedule items can also be used to convey commitments outside of specific time slots (like weekly projects or daily office hours).

Weekly projects

Course schedule

Executives, engineering managers, and product managers leading and coordinating technical AI teams leveraging LLMs

Developers without formal training in machine learning that want to quickly understand the LLM technology in their projects

A gentle introduction to LLMs for professionals pivoting their career to AI from traditional software engineering or other STEM fields

Anyone who can read a spreadsheet that is motivated and deeply interested in how the next wave of computing works

Who is this course for

What you’ll get out of this course

This course breaks down complex AI concepts into easy-to-follow steps using Excel, making it accessible for anyone with a STEM background.

Understand how LLMs work as quickly as possible

Precisely understand in detail the architecture and function of the GPT2 model, including tokenization, embeddings, attention, multi-layer perceptron, and layernorm.

Recognize and explain every component of an LLM

You'll gain a solid understanding of how LLMs function, enabling you to confidently engage with engineers and technical stakeholders.

AI Literacy for managing technical teams

Build a foundation that equips you with the essential knowledge to explore advanced AI topics, undertake AI projects, or pursue further coursework.

Prepare yourself for further exploration, projects, or coursework in artificial intelligence

Master AI Concepts with a Real LLM Implemented Entirely in a Spreadsheet. No Complex Math. Just Clarity. 

Jan 8—17, 2025 (On-demand)

Ishan Anand

Unlock a personalized learning experience.  Dates and times will be customized to the participant.

1-on-1 Premium Version 

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

Cohort 1

Cohort 2

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

Learn to design, monitor, and deploy working AI systems. This transforms them to build robust, real-world LLM apps using prove

May AI Engineer

CEO Hyperskill | Lecturer JetBrains, MIT

Nikolay Vyahhi

I'm passionate about using AI to make learning smarter and more accessible.

CTO Hyperskill | Founder TheOna 

Ruslan Davletshin

I’ve built and deployed LLM systems at scale in production environments. 

AI, ML Engineer & Teaching Expert

Nikalina Ogorodova

I enjoy bridging the gap between complex technology and clear understanding.

AI Engineer & Researher

Ivan Rodin

I have over 7 years of experience in Data Science and Machine Learning.

Bootcamp cuts through AI noise — from foundations to evaluating, securing, and deploying AI systems

June AI Engineer 

Are you an experienced engineer looking to build systems on top of LLMs, not just experiment with AI tools out of the box?In 2026, working with AI increasingly means designing and shipping products powered by LLMs: assistants, RAG pipelines, agentic workflows, evaluation systems. Many engineers use these tools but far fewer know how to structure them into reliable, production-grade systems.This program focuses on that 2026 skillset.The Hyperskill AI Engineering Bootcamp is a 10-week, hands-on program for engineers who want to move to architecting reliable AI systems. You’ll build end-to-end projects using LLMs as components: designing retrieval pipelines, working with vector databases, implementing agents, setting up evals and handling deployment, monitoring and security.The emphasis is on practical system design, trade-offs and failure modes.Modules include: • LLM pipelines &amp; system design • Agents and multi-agent patterns • Vector search &amp; RAG systems • Evaluation, monitoring, security • Deployment of LLM-based applicationsExpect ~10 hours/week of focused work.

10-week Agentic  AI Engineering from experienced educators & practitioners

Design and build functional LLM pipelines end-to-end

Build AI agents with real autonomy using tools, memory, and function calling

Design high-quality context & RAG systems

Build advanced multi-agent & multimodal AI systems

Deploy secure and privacy-first LLM applications

Evaluate, test & monitor AI systems

Comfortable with programming know some Python. New to Python? We’ll give prep materials and 1–2 weeks of free Hyperskill access.

Basic programming experience (Python preferred)

On average learning takes 10–12 hours weekly completing hands-on tasks, projects, and practice at their own pace

Weekly time commitment (10-12 hours)

Live sessions are held in small groups with flexible timing and typically run twice

Availability for live sessions

Software engineers who want to level up from basic LLM usage to building production-ready AI systems, agents, and RAG pipelines

Technical PMs and team leads who want to raise their company’s AI capabilities and confidently drive adoption of modern AI systems

Developers looking to switch jobs by gaining the modern, in-demand AI engineering skills companies hire for today

Learn to achieve reliable AI outcomes without prompt packs, system prompts, or agent tricks—use structured inputs for consistent results.

Creator, AIgogy Framework™ | Advisor

Rockford University

Silali Banerjee

Creator of AIgogy Framework™ for reliable, structured AI outcomes

July2026Cohort

Pretty much anyone using an AI chatbot has gone through the frustration of a response that isn't exactly what they wanted. Problems include hallucinated, drifted, generic, and inconsistent output. Sometimes all of these happen; other times, one or more.People try prompt improvisation, prompt tweaking, prompt packs, prompt libraries, trial and error, tries and retries. Sometimes it works; other times, it does not.Too many details in a prompt cause prompt bloating and do not solve the problem. Adding context helps, but too much context creates problems.AI agents and skills do not solve this problem with open-ended work either.People give up, thinking LLMs are probabilistic and cannot work better.That is not true.It may never be as deterministic as systems in terms of consistency and reliability, but it can be mostly consistent and reliable.Similarly, users can create a path with an LLM using the right framework. Users can guide their request through the LLM toward their desired outcome by using the right framework.Learn to use this science-backed, proven framework.It is based on research (<a target="_blank" rel="noopener noreferrer nofollow" href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6516618">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6516618</a>) and has been tested in practice.<hr>

User's framework for moving beyond trial-and-error prompting with any LLM.

Achieve More Reliable AI Outcomes

Reflect Your Voice and Working Style

Embed Your Judgment and Standards

Reduce Rework and Increase Leverage

Leaders (Decision Owners) Leaders accountable for outcomes who need AI to reflect their judgment without reviewing or correcting every output

Operators (Execution Owners) Operators responsible for consistent delivery who need AI to produce aligned outputs without repeated rework

Knowledge Workers (Expertise Carriers) Experts who need AI to apply their domain knowledge and standards, not generate generic outputs.

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Engineers and engineering leaders who build, deploy, and scale software systems.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

AI Agents & Multimodal LLMs

What you'll learn

Why this topic matters

You'll learn from

Mark Kim-Huang