Course curriculum

    1. Goals & objectives of the course

    2. Motivation: real-world examples and use cases

    3. Introduction to our use case: support automation

    1. Understanding LLM APIs

    2. Introduction to Langchain

    3. Using LLM Chains and Agents

    4. Integrating LLMs into User Interfaces (UI)

    5. Identifying requirements for a software support automation bot

    1. Building a simple baseline

    2. Understanding levers for improving our application

    3. Introduction to embeddings

    4. Similarity and distance metrics

    5. Understanding how LLMs work

    6. Overview of LLMs

    7. Sampling strategies for LLMs

    8. Tracking and managing experiments

    9. When to train or fine-tune your own model?

    1. Regular expressions and fuzzy matching

    2. Model-based evaluation methods

    3. Human evaluation strategies

    4. Comparing evaluation approaches

    5. Incorporating feedback for continuous improvement

    1. How to deploy LLM-powered applications

    2. Overview of monitoring and maintaining LLM-powered applications

    1. Recap of key concepts and techniques

    2. Additional resources for further learning

About this course

  • Free
  • 26 lessons

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, agents 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. Deploy and monitor your application in production.


  • Intermediate Python experience

  • No machine learning skills required


Coming soon!

Stay tuned for an exciting announcement of our instructor!

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 leader in the Polish NLP community. He’s a Kaggle competition winner and 3x Kaggle Master.

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.

Be first in line to unlock your LLM potential and earn your certificate.