Structure of the Course
Main Modules
Module 1 gives you the fundamentals of LLMs. This module is geared towards understanding the architecture of transformer models, including embeddings, similarity, and attention, and some of their applications, such as semantic search.
Module 2 is an introduction to Cohere’s endpoints. It is focused on those endpoints that are used for text representation, such as Embed and Classify. In this module, you’ll find several code labs guided towards building cool applications such as clustering news, classifying text, or semantic search.
Since generation is so important and has been so relevant lately, Module 3 is focused solely on generation. Learn how to build a chatbot from scratch using the Chat endpoint and explore features like defining preambles, streaming, and state management. You will also learn the basics of prompt engineering and how to craft creative and effective prompts to obtain desirable outputs for various tasks.
Once you know how to use Cohere's endpoints to build applications, the next step is to learn how to deploy these applications in order for others to use them. In Module 4, you'll learn how to deploy LLM-powered applications on several platforms and frameworks such as Streamlit and FastAPI.
Search systems have been crucial in computing even before the birth of the internet, for efficiently navigating and retrieving information within vast data sets. In Module 5, you'll learn how to enhance the performance of search systems with LLMs, and also how to use search to improve the results of a generative model, in order to reduce hallucinations and output more exact responses. You'll first learn basic search systems like keyword search. Then you'll learn more powerful search systems that retrieve information based on semantic meaning, such as dense retrieval and reranks. Finally, you'll get to put what you learned into practice in our coding labs, where you'll be searching for answers to queries using a large dataset of Wikipedia articles.
Generative LLMs are powerful and are making a deep impact in many areas. But you must give them the most effective prompts to extract the best performance from them. In Module 6, you'll learn the best practice techniques for constructing an effective prompt to help you get the intended output from a generative model. You'll then learn how to apply these techniques to various use cases, as well as how to chain multiple prompts to unlock even more opportunities to build innovative applications. Finally, you'll learn how to validate and evaluate the outputs generated by an LLM.
Cohere provides a fully managed LLM platform that enables teams to leverage the technology without having to worry about building infrastructure and managing deployments. In Module 7, you'll get a solid overview of the Cohere platform, including the types of foundation models it serves, the available endpoints, and the range of applications that can be built on top of it.
RAG is an approach that significantly reduces the hallucination issue common in LLMs. RAG enables the model to access and utilize supplementary information from external data sources, thereby improving the accuracy of its responses. By the end of this module, you will be able to build RAG-powered applications by leveraging various Cohere endpoints – Chat, Embed, and Rerank, as well as using connectors to connect RAG applications to datastores.
Ways to learn
The material we’ve put together can be used in two ways:
- Sequential: The course starts with the basics of LLMs and their architecture and assumes that you are already familiar with the basics of Machine Learning (ML) and Natural Language Processing (NLP). However, if you’d like to brush up on the basics of ML and NLP, this material is in the Appendix.
- Non-Sequential: If you feel like you already know the basics of NLP and LLMs, or you have a particular project in mind, you can skip ahead to the later modules. If you have a specific goal in mind, you are more than welcome to go directly to that particular module.
Extra Material
We have also gathered some extra material to complement your learning.
If you feel like you need a brush up on the basics on NLP or ML, the Appendix has the material to get you up to speed.
This module contains an introduction to natural language processing, including history, language pre-processing techniques, and the machine learning models that have been used since NLP started, including supervised and unsupervised techniques. It ends with classification, including training classification models, evaluating them, and exploring their applications.
Ready to learn? Go!
We are so excited to have you learning with us. Happy learning!
Updated 5 days ago