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4 LLMs & RAG Projects Every AI Engineer Must Build in 2026
Rating: 4.3 out of 5(98 ratings)
1,724 students

4 LLMs & RAG Projects Every AI Engineer Must Build in 2026

Practice Generative AI and Large Language Models with Real-world Exercises!
Created byOmar Elgendy, Desired AI
Last updated 4/2026
English

What you'll learn

  • Deep Learning
  • Transformers
  • Langchain
  • Large Language Models

Course content

5 sections • 27 lectures • 4h 31m total length

  • Introduction4:13

    Explore four open-source LLM exercises on Google Colab with Kaggle data and Hugging Face models, including question answering on Nvidia docs, text summarization, semantic search, attention is all you need.

  • How to use any dataset on Kaggle in Colab4:55
  • LLMs Training Optimization Methods - Quantization & LoRA11:51
  • What is RAG ?18:50
  • Evaluation Methods for LLMs10:10

    Explore evaluation methods for large language models, including rouge score for text similarity and bleu score for translations, plus benchmarks like glue and super glue to rank models.

  • How many models we can use in RAGs problems?
  • Which of the following is a common use case for Large Language Models (LLMs)

Requirements

  • Python Knowledge
  • Basic Deep Learning Knowledge
  • Transformers knowledge is desirable

Description

Dive into the revolutionary world of Large Language Models (LLMs) with our comprehensive 4-hour workshop, designed to bridge the gap between theoretical knowledge and practical skills. Whether you're a budding data scientist, an AI enthusiast, or a seasoned professional looking to expand your toolkit, this course is tailored to empower you with hands-on experience in leveraging LLMs for a variety of real-world applications.


What You'll Learn:

  • Fundamentals and Advanced Techniques: Start with the basics of Large Language Models, including their architecture and capabilities, before progressing to advanced optimization methods such as Quantization and LoRA.

  • Practical Exercises: Engage in structured exercises using Kaggle datasets in Colab, fine-tuning models for tasks like question answering and text summarization with QLoRA, and exploring cutting-edge concepts such as Retrieval Augmented Generation (RAG).

  • Real-World Applications: Tackle engaging projects like building a semantic search engine to find movies and developing a chat interface with scholarly articles, applying your knowledge in tangible, impactful ways.

  • Model Publication: As a bonus, learn how to share your fine-tuned models with the world through Huggingface, enhancing your visibility in the AI community.

Intended Learners:

This course is perfect for individuals looking to deepen their understanding of LLMs and apply these models in innovative ways. Ideal for AI professionals, data scientists, and researchers eager to expand their skills and apply LLMs to solve complex problems.

Who this course is for:

  • Deep learning Engineers
  • Deep Learning enthusiasts
  • Machine Learning Engineers
  • Artificial intelligence Engineers

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