Build With LLMs: AI Engineering Patterns for Scrappy Developers

Cohort-based Course

Learn the key AI engineering patterns by building an app that extracts insights from unstructured text. Practical AI insights for developers

This course is popular

43 people enrolled last week.

Taught devs, founders, and managers at

Google
Zillow
Meta
NVIDIA
Microsoft

Course overview

Explore AI engineering patterns while building real systems

You're going to create an "Insights Extraction AI App" ("Unstructured Text Data" to "Structured Insights") using a modern stack of tools and AI engineering patterns


An end-to-end deployed (live on the web) application that converts unstructured data (podcast, meeting notes, customer feedback, essays, etc.) and outputs structured insights (tweets, product descriptions, summaries, etc.).


The goal is to build & learn a flexible application structure so you can extend to your use case.


Example applications you'll be able to extend this course's code to: Meeting "Next Steps" extractor, Podcast Summarizer, Customer Survey Trends Extractor, Extract class lessons from lectures, Data stream classifier, Custom apps with your own data


AI Engineering Patterns we'll cover:

* Prompting - The basic 3 step pattern for writing development prompts + optimizers, structured outputs

* Observability - To manage costs, tracing and debugging

* Retrieval/Chains - Adding more context to your LLMs and connecting multiple LLM calls together for a unified outcome

* Model Choice - Tribal knowledge about when to pick certain models over another (spoiler: it depends on your use case)

* Evals - The new style of unit tests for your LLM apps

* Mindset - You'll learn how to approach novel problems with AI


We promise three outcomes with this course:

* You’ll launch an AI-powered app designed to teach you the foundations to build your own tools

* You’ll surround yourself with a new group of friends: other builders who are as passionate as you (and hold you accountable)

* You’ll 2x your likelihood to ship by experiencing first-hand the greatest trait a builder can have: a bias for action


You'll get experience building with a modern AI powered development stack:

* Cursor - Write code, but with ChatGPT by your side

* v0 - Build user interfaces with words, not code

* LLMs - Outsource intelligence to Large Language Models APIs like OpenAI/Anthropic

* LangChain/LangSmith - Orchestrate and observe your LLM calls, ensuring your costs don't run out of control

* Railway/Vercel - Deploy your apps

* Supabase - Simple postgres database

* Zapier - Simple ways to quickly integrate your application with other tools

* Anthropic Artifacts - For prototyping mini applications


"The best way to get good at something is usually to just practice actually doing the thing in question. A lot of very capable people outsmart themselves with complex plans that involve working a lot on fake prerequisites." - Sam Altman 10/27/2024


It’s no longer skill level that's holding you back, it’s just your desire to take a step forward.


Along the journey, you’ll have access to mentors, successful builders that have built apps, a network of like-minded people, creative workshops, and 1:1 sessions.


This program is a fast-paced, highly engaging program, but you don’t need to take time off work to participate. We’ve designed the program to be as flexible as possible for those who work full-time or have busy schedules.


With that said, we encourage you to commit to at least 4-5 hours of your spare time each week, for deep work, feedback groups, and live sessions.


In addition to the course material, this cohort includes:

* Code Templates - All materials shown in class will be available for download so you can continue to quickly build after the class

* Slack - A channel for you to ask questions, connect with other builders, and get support on your projects

* Guests - Each week we'll feature an expert from the industry to talk about AI Engineering


This course is not:

* Learn to be a programmer from scratch

* Learn the absolute basics

* Learn how to fine tune LLMs


On the traditional SWE side, we will build our backend with Python and Postgres, frontend with Next.js, and deployed on Railway. Though this course is taught in python, you have the flexibility to bring whatever tool stack you're comfortable with.


Who you are

* You have a intermediate level understanding of Python + Javascript (React is a plus)

* You're not afraid to roll up your sleeves and learn new technology

* You value shipping something quick

* You want to build AI-powered products

* You're ready to break a big problem down into shippable chunks

Who is this course for

01

Scrappy Developers. You're a hands-on builder ready to turn your skills into real AI applications, even if development isn't your day job

02

Technical Leaders. CTOs and technical managers who are struggling to elevate the AI conversations with their teams

03

Experienced Developers. Software Engineers who want to build AI products but haven't dove into an AI focused tool stack yet

What you’ll get out of this course

Playbook of best practices for 5 areas of LLM development

Become proficient in prompting, observability, chaining, model choice, structured outputs and scaling apps

A deployed "Insight Extraction" app live on the web

You'll build your own tool which will start saving you time from Day 1. We'll walk through the steps of deploying your application on a custom domain (Ex: YourCoolAppYouBuilt.com)

A survey of modern AI tools that will speed boost your workflow

You'll gain hands-on experience with Cursor, v0, LLMs (OpenAI, Anthropic, Google), LangChain & LangSmith, Railway/Vercel, Supabase, Zapier, and Anthropic's Artifacts

Instructions to scale your LLM apps

We will run through step by step instructions on how to reduce the cost and latency of your LLM apps while maintaining (or even increasing) performance. This is critical to keep your LLM costs under control

This course includes

13 interactive live sessions

Lifetime access to course materials

19 in-depth lessons

Direct access to instructor

1 projects to apply learning

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

Jan 6—Jan 12

    AI Engineering Foundations

    4 items

    Jan

    6

    Office Hours

    Mon 1/611:00 PM—12:00 AM (UTC)
    Optional

    Jan

    9

    Guest Speaker

    Thu 1/96:00 PM—7:00 PM (UTC)
    Optional

Week 2

Jan 13—Jan 19

    Prompt Engineering For Development

    4 items

    Jan

    13

    Office Hours

    Mon 1/1311:00 PM—12:00 AM (UTC)
    Optional

    Jan

    16

    Guest Speaker

    Thu 1/166:00 PM—7:00 PM (UTC)
    Optional

Week 3

Jan 20—Jan 26

    AI Engineering Architecture + Observability

    3 items

    Jan

    20

    Office Hours

    Mon 1/2011:00 PM—12:00 AM (UTC)
    Optional

    Jan

    23

    Guest Speaker (Industry)

    Thu 1/236:00 PM—7:00 PM (UTC)
    Optional

Week 4

Jan 27—Feb 2

    Retrieval (RAG) Basics (w/ Preview of Advanced)

    2 items

    Jan

    27

    Office Hours

    Mon 1/2711:00 PM—12:00 AM (UTC)
    Optional

    Jan

    30

    Guest Speaker (Industry)

    Thu 1/306:00 PM—7:00 PM (UTC)
    Optional

Week 5

Feb 3—Feb 9

    Scaling AI Apps - Better Costs, Latency & Performance

    3 items

    Feb

    3

    Office Hours

    Mon 2/311:00 PM—12:00 AM (UTC)
    Optional

    Feb

    6

    Guest Speaker

    Thu 2/66:00 PM—7:00 PM (UTC)
    Optional

Week 6

Feb 10—Feb 15

    Integration & Polish

    2 items

    Demo preparation

    2 items

    Feb

    13

    AI Show & Tell

    Thu 2/1311:00 PM—12:30 AM (UTC)

    Feb

    10

    Office Hours

    Mon 2/1011:00 PM—12:00 AM (UTC)
    Optional

    Feb

    13

    Guest Speaker

    Thu 2/136:00 PM—7:00 PM (UTC)
    Optional

What people are saying

        Completely agree! @GregKamradt is awesome. Is @GregKamradt the best langchain webinar guest?
Harrison Chase

Harrison Chase

CEO, LangChain
        [Greg] had widespread influence across our product and marketing orgs in helping us build product and tell a better story. Greg is an amazing army of one.
Wade Foster

Wade Foster

CEO, Zapier
        Greg, you're not going to believe me, but it's true. I've watched 20+ hours of your YouTube videos. Have learned a lot from them. They are amazingly useful. Thanks for helping me become a bit less clueless on this stuff. Cheers.
Dharmesh Shah

Dharmesh Shah

CTO, Hubspot
        The best tutorials on building LLM powered applications 📚 @GregKamradt is an incredible teacher of @LangChainAI: ✅ Top down & applied series ✅ Amazing teaching style ✅ Very practical examples
Sanyam Bhutani

Sanyam Bhutani

Partner Engineer, Llama @ Meta

Meet your instructor

Greg Kamradt

Greg Kamradt

Founder @ Leverage, AI Educator

Greg Kamradt, AI Product & Education Leader @ Leverage, has helped over +100K developers build AI applications from companies like Google, Zillow, Meta, Nvidia, and Microsoft. Currently, he is CEO of Leverage, an AI Product & Education Studio. Previously he was Director of Growth at Salesforce.


Greg authored the original "Needle In A Haystack" analysis, testing the long-context utilization of LLMs. Greg also co-leads ARC Prize, $1M competition to beat the #1 AGI benchmark, ARC-AGI.


With over 51K subscribers on YouTube, Greg is an active leader & voice in building AI applications.

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Course schedule

4-6 hours per week

  • Saturdays

    10:00am - 11:00am PT

    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

  • Jan 6, 2024

    We'll kick off the course in the new year

  • Weekly projects

    3 hours per week

    Each week will consistent of live instruction, video overviews, and "homework" (the fun kind) through practicing to build.

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

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Build With LLMs: AI Engineering Patterns for Scrappy Developers