Building Generative AI Applications with a Problem-First Approach

New
·

6 Weeks

·

Cohort-based Course

Learn to design & build generative AI solutions that solve real-world challenges with a focus on impact, not trends.

Worked/Taught at

Amazon Web Services
Google
Samsung
University of Oxford
MIT research group

Course overview

Design and build impactful generative AI systems to solve business problems.

AI is revolutionizing the way we approach technology. In the coming years, most software systems will be supercharged with some form of AI. This shift will create a growing demand for individuals who can go beyond surface-level knowledge and apply AI effectively to solve real business problems while addressing practical constraints.


In this course, you’ll learn practical concepts such as prompt engineering, RAG, agents, and fine-tuning—but always with a focus on solving real-world problems. Rather than just covering the "what," we’ll dive into the "when" and "how," equipping you to make informed decisions tailored to business needs and constraints.


What Makes This Course Unique


Solving Real Business Challenges, Not Just Concepts

While most courses stop at teaching tools and frameworks, this course goes further by focusing on solving real-world business problems. You’ll tackle practical constraints like cost, scalability, latency, and performance, learning to design AI solutions tailored to real use cases that deliver value.


Hands-On Product Development

You’ll build a real-world product from scratch (low code options available). Our tutorials will guide you through creating your own Perplexity (AI-powered search system), giving you practical experience with techniques like RAG & agents.


Collaborative Team Workshop

Experience what it’s like to solve a business problem in a real-world setting. In a collaborative workshop, you’ll work with others to design and develop AI solutions, simulating how teams take AI projects from 0 to 1. 


Capstone Project

Learn how to connect cutting-edge research with real-world applications. For the capstone, you’ll use our curated list of the latest research papers to design and implement solutions for practical business use cases, 




Who This Course Is Not For


Those Seeking Heavy Theoretical Knowledge: This course emphasizes applied learning and practical problem-solving, not deep dives into theoretical topics like transformer architecture, pre/post-training optimization, inference techniques, or alignment.


Those Who Have Never Coded Before: While we provide low-code options, this course assumes you have some coding experience. It’s not suitable for those who have never written or worked with even basic code.


Individuals Expecting Deep AI Research Focus: While we’ll cover cutting-edge techniques, this course is centered on applying AI to business problems, not research-heavy exploration.


Scaling and Ops Enthusiasts: This course does not focus heavily on scaling or operational aspects (i.e., LLMOps). Deployment will be covered at a high level, but not in-depth.

Who is this course for

01

Software/AI Engineers, Strategists, Data Professionals, Solution Architects and Consultants who want to master AI system design

02

Business Leaders and Product Managers seeking to gain the technical understanding needed to make informed decisions & lead AI initiatives

03

Entrepreneurs looking to understand common generative AI use cases and learn how to develop and implement AI-powered solutions

What you’ll get out of this course

Master Applied Generative AI Concepts

Learn applied generative AI techniques like prompt engineering, RAG, agents, and fine-tuning, with a focus on applying them effectively to real-world business scenarios. Understand how to navigate constraints like cost, latency, and performance constraints in AI systems.

Apply Concepts to Build an AI-Powered Search System

While learning applied AI concepts, we’ll put them into action by building a Perplexity-like AI-powered search system through detailed, hands-on tutorials that demonstrate their practical application (Low code options will be provided)

Tackle Business Use-Cases with AI

Participate in brainstorming sessions and team huddles to approach hypothetical business problems. Learn to identify the right AI solutions for specific use cases, leveraging low-code options where applicable.

Understanding Challenges and Effective Evaluation

Gain a deep understanding of key challenges in building AI systems, including handling hallucinations, adversarial attacks, security, privacy issues etc., and learn best practices to evaluate AI solutions comprehensively

Final Capstone Project

You’ll design and demo a solution for a hypothetical business problem, integrating insights from relevant research papers in my curated list, hand-picked for enterprise relevance. This project will also involve addressing challenges and evaluation methods.

This course includes

10 interactive live sessions

Lifetime access to course materials

45 in-depth lessons

Direct access to instructor

Projects to apply learnings

Guided feedback & reflection

Private community of peers

Course certificate upon completion

Maven Satisfaction Guarantee

This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.

Course syllabus

Week 1

Feb 21—Feb 23

    Generative AI Landscape and Evolution

    3 items

    Application Categories: Where Generative AI Adds Value

    3 items

    Prompt Engineering in 2025 (Advanced Methods & Automatic Optimization)

    3 items

    Business Use Cases & Challenges with Prompting

    3 items

    Feb

    22

    Live Lecture 1

    Sat 2/226:00 PM—7:00 PM (UTC)

    Assignment 1: Build Your v1 AI-Powered Search System

    1 item

Week 2

Feb 24—Mar 2

    Introduction to RAG & Vector Databases

    3 items

    Advanced Techniques (Multimodal RAG, Semantic Caching, Agentic RAG etc.)

    2 items

    Why RAG is an Invaluable Solution for Business Use Cases

    2 items

    RAG Real-World Challenges & Solutions

    2 items

    Mar

    1

    Live Lecture 2

    Sat 3/112:00 AM—1:00 AM (UTC)

    Mar

    1

    Live Lecture 3 + Office Hours

    Sat 3/15:00 PM—6:30 PM (UTC)

    Assignment 2: Build Your v2 AI-Powered Search System using advanced RAG

    1 item

Week 3

Mar 3—Mar 9

    Agents: Types, Designs and Multi-Agent Systems.

    3 items

    Technical and Operational Challenges of Agentic Pipelines

    1 item

    How, When, and When Not to Build Agents for Business Use-Cases

    3 items

    Evaluating Generative AI Applications

    3 items

    Mar

    8

    Live Lecture 4

    Sat 3/812:00 AM—1:00 AM (UTC)

    Mar

    8

    Live Lecture 5 + Office Hours

    Sat 3/85:00 PM—6:30 PM (UTC)

    Assignment 3: Build Your v3 AI-Powered Search System using Agents

    1 item

Week 4

Mar 10—Mar 16

    Fine-Tuning: Methods and Use Cases

    2 items

    When Fine-Tuning is Valuable for Business Use-Cases (& When it's Not)

    3 items

    Synthetic Data Generation for Fine-Tuning

    1 item

    Decision Matrix for Building Generative AI Systems

    2 items

    Mar

    15

    Live Lecture 6 + Office Hours

    Sat 3/154:00 PM—5:30 PM (UTC)

Week 5

Mar 17—Mar 23

    Capstone Project Overview and Deliverables

    1 item

    Emerging Frontiers in AI: What’s Ahead

    2 items

    Mar

    22

    Live Lecture 7 + Office Hours

    Sat 3/224:00 PM—5:30 PM (UTC)

    Mar

    23

    Optional: Team Workshop: Collaborate in teams to design a generative AI solution for a business problem, with steps, evaluation strategies, and potential challenges

    Sun 3/234:00 PM—5:30 PM (UTC)
    Optional

Week 6

Mar 24—Mar 28

    Capstone Project Due

    0 items

    Mar

    27

    Office Hours

    Thu 3/2711:00 PM—12:00 AM (UTC)

    Mar

    28

    Optional: Guest Session (Enterprise AI Leader)

    Fri 3/2811:00 PM—12:00 AM (UTC)
    Optional

Meet your instructor

Aishwarya Naresh Reganti

Aishwarya Naresh Reganti

Tech Lead @ AWS | Lecturer | Advisor | Researcher | Speaker | Investor

Aishwarya Naresh Reganti is an Applied Science Tech Lead at the AWS Generative AI Innovation Center (GenAIIC), where she leads initiatives to develop and deploy production-ready generative AI solutions for AWS clients. With over 9 years of experience in machine learning, she has published more than 35 research papers at top-tier AI conferences, including NeurIPS, AAAI, and CVPR.


Aishwarya has taught professional courses on generative AI at renowned institutions like MIT and Oxford. She has also designed free courses that have reached over 8,000 students globally and have formed the foundation for several academic programs and industry training curricula.


Recognized as one of the most prominent voices in enterprise AI, with over 80,000 professionals following her on LinkedIn, she is a sought-after thought leader frequently invited to speak at leading conferences and events, including TEDx, MLOps World, and ReWork.


Aishwarya actively collaborates with leading research professors and provides strategic advisory to organizations, enabling them to harness AI effectively to address complex business challenges.


Kiriti Badam

Kiriti Badam

Founding Engineer @ Kumo.ai | Ex-Google

With over a decade of experience designing high-impact and transformative enterprise AI systems, Kiriti Badam is a seasoned expert in AI-centric infrastructure, specializing in large-scale compute, data engineering, and storage systems. At Kumo.ai, a Forbes AI 50 startup, he leads the development of infrastructure capable of training hundreds of models daily, driving significant ARR growth for enterprises.


Kiriti brings a unique blend of startup agility and large-scale enterprise expertise, having worked across companies of varying sizes, including Kumo.ai, Google, Samsung, and Databricks. At Google Ads, he developed globally distributed key-value stores that powered advertising systems generating XX billion dollars in annual revenue.


Kiriti holds a Master’s degree from Carnegie Mellon University and a Bachelor’s degree from IIT Madras, where his research focused on cutting-edge storage systems and distributed databases for AI workloads. A trusted advisor and mentor, he guides startups and organizations in building impactful AI infrastructure, achieving product-market fit, and crafting robust product development strategies.

A pattern of wavy dots

Join an upcoming cohort

Building Generative AI Applications with a Problem-First Approach

Cohort 1

$950

Dates

Feb 22—Mar 29, 2025

Payment Deadline

Feb 21, 2025
Get reimbursed

Course schedule

5-8 hours per week

  • Fridays and Saturdays (Mostly)

    Fridays: 4–5 PM PT

    Saturdays: 9–10:30 AM PT


    You can also attend optional guest sessions and team workshops scheduled outside of lecture hours.


  • Weekly projects

    2 hours per week


Free resource

Our Curated Generative AI Free-Resource Bundle

Access our curated collection of top reading materials, roadmaps, research papers, and more!


Get this free resource

Learning is better with cohorts

Learning is better with cohorts

Active hands-on learning

This course builds on live workshops and hands-on projects

Interactive and project-based

You’ll be interacting with other learners through breakout rooms and project teams

Learn with a cohort of peers

Join a community of like-minded people who want to learn and grow alongside you

Frequently Asked Questions

Stay in the loop

Sign up to be the first to know about course updates.

A pattern of wavy dots

Join an upcoming cohort

Building Generative AI Applications with a Problem-First Approach

Cohort 1

$950

Dates

Feb 22—Mar 29, 2025

Payment Deadline

Feb 21, 2025
Get reimbursed

$950

6 Weeks