Course curriculum

    1. Introduction & goals of the course

    2. Demo of an LLM-powered app

    3. W&B Prompts for LLMs

    4. Quick question before you continue

    1. Understanding large language models

    2. Tokenization in action: converting text to tokens

    3. Sampling methods: generating text with LLMs

    4. Sampling practice

    5. Using chat completion API

    6. Prompting LLMs

    7. Prompt engineering practice: zero shot and few shot

    8. Prompt engineering practice: level 5 prompts

    9. Looking at the results

    1. Why an API call alone is not enough

    2. Designing application architecture

    3. Overview of vector databases

    4. Parsing documents

    5. Understanding retrieval question answering

    6. Ingesting documentation

    7. Building a web application

    1. Evaluating LLM Apps

    2. Identifying areas of improvement

    3. Evaluation practice

    4. Analyzing evaluation results

    5. Controlling LLM outputs

    6. Safety considerations and prompt injection

    7. Wandbot overview

    1. Final quiz

    2. Project assignment

    1. Course recap and next steps

    2. Additional resources for further learning

About this course

  • Free
  • 31 lessons
  • 2 hours of video content

Your Goals

Sign up for this free Weights & Biases course to:

  • Understand LLM-powered applications

    Learn the fundamentals of LLM-powered applications, including APIs, chains and prompt engineering.

  • Build your own app

    See how we develop a support automation bot for a software company, and build your own app.

  • Experiment, evaluate, and deploy your solution

    Improve your LLM-powered app with structured experiments and evaluation.

Prerequisites

  • Intermediate Python experience

  • No machine learning skills required

Guest Instructors

Shreya Rajpal

Creator of 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 of 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.

Shahram Anver

Co-Creator of Rebuff

Shahram Anver is a co-creator of Rebuff. He loves building meaningful products. Proficient at juggling business, engineering and data challenges.

Course Authors

Darek Kłeczek

Machine Learning Engineer

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.

Bharat Ramanathan

Machine Learning Engineer

Bharat is a Machine Learning 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

Machine Learning Engineer

Thomas Capelle is a Machine Learning 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.

Reviews

5 star rating

Unlocking the Power of Language Models: Learn the basics and feel empowered by W&B metrics.

Fergus Findley

I recently took the "Building LLM-Powered Apps" free online course offered by Weights & Biases, and I must say it exceeded my expectations. The course provided a comprehensive understanding of Large Language Models, equipping me with valuable ...

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I recently took the "Building LLM-Powered Apps" free online course offered by Weights & Biases, and I must say it exceeded my expectations. The course provided a comprehensive understanding of Large Language Models, equipping me with valuable knowledge and insights. One standout feature of the course was its introduction to the impressive capabilities of W&B, particularly its ability to store and track important metrics of the model. This not only enhanced my understanding of LLMs but also empowered me to optimize and improve my own applications. I highly recommend this course to anyone looking to delve into the world of language models and leverage the power of W&B.

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5 star rating

Amazing course with valuable content

Tony AdAstra

Fantastic for those building in the LLM ecosystem. Had a great experience learning about techniques to make a great production-grade application. One suggestion - having subtitles for the videos would make them easier to follow!

Fantastic for those building in the LLM ecosystem. Had a great experience learning about techniques to make a great production-grade application. One suggestion - having subtitles for the videos would make them easier to follow!

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5 star rating

Loved the course!

Atharva Ingle

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5 star rating

Great and simply explained with code examples

Google User

I love the course. It basically focus on prompt engineering using Python. It's also clear and simple. It doesn't require any advanced math from high school you already forgot.

I love the course. It basically focus on prompt engineering using Python. It's also clear and simple. It doesn't require any advanced math from high school you already forgot.

Read Less