Greg Kamradt

Cohort 1

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 &amp; 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&nbsp;designed to teach you the foundations to build your own tools* You’ll surround yourself with a new&nbsp;group of friends:&nbsp;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:&nbsp;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&nbsp;at least&nbsp;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

Explore AI engineering patterns while building real systems

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

Saturdays

We'll kick off the course in the new year

Jan 6, 2024

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

Weekly projects

Course schedule

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

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

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

Who is this course for

What you’ll get out of this course

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

Playbook of best practices for 5 areas of LLM development

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 deployed "Insight Extraction" app live on the web

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

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

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

Instructions to scale your LLM apps

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

Founder @ Leverage, AI Educator

Turn Unstructured Text Into Insights With AI

Extract insights from unstructured data sources like transcripts, meetings or notes

Unstructured > Structure data mindset

Build a working prompt to extract insights from a body of text via the "meta prompting" method

Insight Extraction Prompting

Get an introductory understanding of optimizing prompts with automated tools

Learn directly from Greg Kamradt

Greg Kamradt

Courses

Build With LLMs: AI Engineering Patterns for Scrappy Developers

Lightning Lessons

Turn Unstructured Text Into Insights With AI