Building LLM-powered apps

Learn how to build LLM-powered applications using LLM APIs, Langchain and Weights & Biases LLM tooling. This course will guide you through the entire process of designing, experimenting, and evaluating LLM-based apps.

3 Hours

Free

Curriculum

Introduction to LLM-powered applications
Unpacking the large language model APIs
Building a baseline LLM application
Enhancing and optimizing LLM applications

Understand LLM-powered applications
Build your own app
Experiment, evaluate, and deploy your solution

In partnership with

Awesome course.

Much Apperciated the hardwork that has been put for this course. It covers all the topics who is looking to excel in LLM apps( begineer and intermediate). All the content provided in the course were so highly organised and the details of the information will encourage the learners to seek more knowledge in LLM.

Great start for building Apps

As a seasoned developer but AI-app newbie, this course really did a great job introducing me to the concepts of LLM development. The instructor explained things well and there was a good balance between high-level concepts and just enough technical detail to help me understand why I was doing a task. I recommend this to anyone getting started building LLM apps

Great hands-on course!

Learn how to create high quality synthetic data from your own doc and train and evaluate a Q&A chatbot with a simple user interface. It is a hands on course about OpenAI API, LangChain, Vector database Chroma and user interface library Gradio

Simple and interesting course!

Until now I found the course really simple and understandable even though I am not an expert on LLM and in particular on ML.

A thorough course.

ML Engineer.

Good training material for a high-level understanding of LLM-Powered Apps.

One of the good sources to start with.

Dear Instructors, I am very thankful to you all for your great efforts to impart the fundamental concepts of LLMs and application development using the same. Please create advanced courses to support active learners like us.

LLMs and W&B

Great content and recommendations on how to build LLM-powered apps and experiment tracking with W&B. Nice videos, reading material and invited speakers. It goes through tokenization, prompting, 5-level prompt, vector databases, retrieval,building a web with gradio, evaluating and analyzing LLM outputs (including safety and security).

Really good one.

A much needed course. Best parts are the topics on doing error analysis and evaluation of LLM outputs, using guardrails and (hopefully) preventing prompt injection. Looking forward to the next course.

Darek Kłeczek

MLE Weights & Biases

Darek Kłeczek is a Machine Learning Engineer at Weights & Biases, where he leads the W&B education program. Previously, he applied machine learning across supply chain, manufacturing, legal, and commercial use cases. He also worked on operationalizing machine learning at P&G. Darek contributed the first Polish versions of BERT and GPT language models and is a Kaggle Competition Grandmaster.

Shreya Rajpal

CEO and Cofounder Guardrails AI

Shreya Rajpal is the creator and maintainer of Guardrails AI, an open source platform developed to ensure increased safety, reliability, and robustness of large language models in real-world applications. Her expertise spans a decade in the field of machine learning and AI. Most recently, she was the founding engineer at Predibase, where she led the ML infrastructure team. In earlier roles, she was part of the cross-functional ML team within Apple's Special Projects Group and developed computer vision models for autonomous driving perception systems at Drive.ai.

Anton Troynikov

Co-founder Chroma

Anton Troynikov is the cofounder of Chroma, an open source embeddings store. Previously, Anton worked on robotics with a focus on 3D computer vision. He doesn’t believe AI is going to kill us all.

Bharat Ramanathan

AI Engineer Weights & Biases

Bharat is an AI Engineer at Weights & Biases, where he built and manages Wandbot, a technical support bot that can run in Discord, Slack, ChatGPT and Zendesk. Currently also pursuing a Data Science Master's at Harvard Extension School. Bharat is an outdoor enthusiast who enjoys reading, rock climbing, swimming, and biking.

Thomas Capelle

AI Engineer Weights & Biases

Thomas Capelle is an AI Engineer at Weights & Biases working on the Growth Team. He’s a contributor to fastai library and a maintainer of wandb/examples repository. His focus is on MLOps, wandb applications in industry and fun deep learning in general. Previously he was using deep learning to solve short term forecasting for solar energy at SteadySun. He has a background in Urban Planning, Combinatorial Optimization, Transportation Economics and Applied Math.

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Building LLM-powered apps

Curriculum

Darek Kłeczek

Shreya Rajpal

Anton Troynikov

Bharat Ramanathan

Thomas Capelle

AI Engineering: Agents

LLM apps: Evaluation

RAG++ : From POC to production

Developer’s guide to LLM prompting

LLM engineering: Structured outputs

Training and fine-tuning LLMs

Weights & Biases 101: Weave

The Platform

Article

Resources

Company

Learn more

Building LLM-powered apps

Curriculum

Darek Kłeczek

AI Engineering: Agents

LLM apps: Evaluation

RAG++ : From POC to production

Developer’s guide to LLM prompting

LLM engineering: Structured outputs

Training and fine-tuning LLMs

Weights & Biases 101: Weave

The Platform

Article

Resources

Company