| Introduction to Large Language Models (LLMs) |
Various online courses (e.g., Coursera, edX) |
Beginner/Intermediate |
Varies (4-8 weeks) |
LLMs, transformers, attention mechanisms, language modeling, tokenization |
Provides foundational knowledge of LLMs, their architecture, and capabilities. Essential before tackling fine-tuning or RAG. |
| Natural Language Processing (NLP) Fundamentals |
Various online courses (e.g., Stanford NLP) |
Intermediate |
Varies (6-10 weeks) |
NLP, text preprocessing, word embeddings, sentiment analysis, named entity recognition |
Necessary for understanding how LLMs process and understand text. Many RAG techniques rely on NLP for data preprocessing and query understanding. |
| Fine-tuning LLMs |
Hugging Face Course, Papers with Code |
Intermediate/Advanced |
Varies (2-4 weeks) |
Fine-tuning, transfer learning, hyperparameter tuning, model evaluation |
Teaches you how to adapt pre-trained LLMs to specific tasks, crucial for building a high-performing RAG system. Learn techniques like prompt engineering. |
| Retrieval Augmented Generation (RAG) Techniques |
Research Papers, Blogs, Tutorials |
Advanced |
Varies (Ongoing Study) |
RAG, vector databases, embedding generation, knowledge retrieval, question answering |
Focuses on the architecture and implementation of RAG pipelines. You'll learn to select and integrate components like vector databases (e.g., Pinecone, Weaviate) and retrieval methods. |
| Python for Data Science (if needed) |
DataCamp, Codecademy, Fast.ai |
Intermediate |
Varies (2-4 weeks) |
Python, pandas, numpy, scikit-learn |
Reinforce your Python skills for data manipulation and model building within your RAG pipeline. |
| Vector Databases (if needed) |
Pinecone, Weaviate documentation |
Intermediate |
Varies (1-2 weeks) |
Vector databases, similarity search, indexing, scalability |
Understanding vector databases is crucial for efficient knowledge retrieval in a RAG pipeline. Learn about different databases and their strengths. |
Potential Data Format that gives courses to take and reasoning. Will be replace with LPI Dataset courses/infoy
input: I want to learn about llm and how to finetune them. I'm intermediate and i want to build a rag pipeline
output: Given your intermediate level and interest in building a RAG pipeline using LLMs, I recommend the following courses and modules: