Large Language Models: A Self-Study Roadmap

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Large language models are a big step forward in artificial intelligence. They can predict and generate text that sounds like it was written by a human. LLMs learn the rules of language, like grammar and meaning, which allows them to perform many tasks. They can answer questions, summarize long texts, and even create stories. The growing need for automatically generated and organized content is driving the expansion of the large language model market. According to one report, Large Language Model (LLM) Market Size & Forecast:

“The global LLM Market is currently witnessing robust growth, with estimates indicating a substantial increase in market size. Projections suggest a notable expansion in market value, from USD 6.4 billion in 2024 to USD 36.1 billion by 2030, reflecting a substantial CAGR of 33.2% over the forecast period”

This means 2025 might be the best year to start learning LLMs. Learning advanced concepts of LLMs includes a structured, stepwise approach that includes concepts, models, training, and optimization as well as deployment and advanced retrieval methods. This roadmap presents a step-by-step method to gain expertise in LLMs. So, let's get started.

Step 1: Cover the Fundamentals

 
You can skip this step if you already know the basics of programming, machine learning, and natural language processing. However, if you are new to these concepts consider learning them from the following resources:

• Programming: You need to learn the basics of programming in Python, the most popular programming language for machine learning. These resources can help you learn Python:
  • Learn Python - Full Course for Beginners [Tutorial] - YouTube (Recommended)
  • Python Crash Course For Beginners - YouTube
  • TEXTBOOK: Learn Python The Hard Way
• Machine Learning: After you learn programming, you have to cover the basic concepts of machine learning before moving on with LLMs. The key here is to focus on concepts like supervised vs. unsupervised learning, regression, classification, clustering, and model evaluation. The best course I found to learn the basics of ML is:
  • Machine Learning Specialization by Andrew Ng | Coursera - It is a paid course that you can buy in case you need a certification, but fortunately, I have found it on YouTube for free too: Machine Learning by Professor Andrew Ng
• Natural Language Processing: It is very important to learn the fundamental topics of NLP if you want to learn LLMs. Focus on the key concepts: tokenization, word embeddings, attention mechanisms, etc. I have given a few resources that might help you learn NLP:
  • Coursera: DeepLearning.AI Natural Language Processing Specialization - Focuses on NLP techniques and applications (Recommended)
  • Stanford CS224n (YouTube): Natural Language Processing with Deep Learning - A comprehensive lecture series on NLP with deep learning.

Step 2: Understand Core Architectures Behind Large Language Models

 
Large language models rely on various architectures, with transformers being the most prominent foundation. Understanding these different architectural approaches is essential for working effectively with modern LLMs. Here are the key topics and resources to enhance your understanding:

• Understand transformer architecture and emphasize on understanding self-attention, multi-head attention, and positional encoding.
• Start with Attention Is All You Need, then explore different architectural variants: decoder-only models (GPT series), encoder-only models (BERT), and encoder-decoder models (T5, BART).
• Use libraries like Hugging Face's Transformers to access and implement various model architectures.
• Practice fine-tuning different architectures for specific tasks like classification, generation, and summarization.

Recommended Learning Resources

• The Illustrated Transformer (Blog & Visual Guide): A must-read visual explanation of transformer models.
• Transformers Explained – Yannic Kilcher: Explaining the implementation of “Attention is All You Need”: An accessible breakdown of the transformer paper (Recommended).
• Language Models are Few-Shot Learners - Decoder-Only Architectures (GPT Series).
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer - T5.
• Hugging Face Tutorial (2024) - This comprehensive guide covers various NLP tasks, including building a sentiment analysis model with Hugging Face (Recommended).
• Fine-Tuning BERT for Text Classification - A video guide explaining how to adapt BERT for text classification using an example to classify phishing URLs (Recommended).
• Fine tuning gpt2 | Transformers huggingface | conversational chatbot | GPT2LMHeadModel - A video guide explaining the fine tuning of the gpt2 model to create a conversational chatbot.

Step 3: Specializing in Large Language Models

 
With the basics in place, it’s time to focus specifically on LLMs. These courses are designed to deepen your understanding of their architecture, ethical implications, and real-world applications:

• LLM University – Cohere (Recommended): Offers both a sequential track for newcomers and a non-sequential, application-driven path for seasoned professionals. It provides a structured exploration of both the theoretical and practical aspects of LLMs.
• Stanford CS324: Large Language Models (Recommended): A comprehensive course exploring the theory, ethics, and hands-on practice of LLMs. You will learn how to build and evaluate LLMs.
• Maxime Labonne Guide (Recommended): This guide provides a clear roadmap for two career paths: LLM Scientist and LLM Engineer. The LLM Scientist path is for those who want to build advanced language models using the latest techniques. The LLM Engineer path focuses on creating and deploying applications that use LLMs. It also includes The LLM Engineer’s Handbook, which takes you step by step from designing to launching LLM-based applications.
• Princeton COS597G: Understanding Large Language Models: A graduate-level course that covers models like BERT, GPT, T5, and more. It is Ideal for those aiming to engage in deep technical research, this course explores both the capabilities and limitations of LLMs.
• Fine Tuning LLM Models – Generative AI Course When working with LLMs, you will often need to fine-tune LLMs, so consider learning efficient fine-tuning techniques such as LoRA and QLoRA, as well as model quantization techniques. These approaches can help reduce model size and computational requirements while maintaining performance. This course will teach you fine-tuning using QLoRA and LoRA, as well as Quantization using LLama2, Gradient, and the Google Gemma model.
• Finetune LLMs to teach them ANYTHING with Huggingface and Pytorch | Step-by-step tutorial: It provides a comprehensive guide on fine-tuning LLMs using Hugging Face and PyTorch. It covers the entire process, from data preparation to model training and evaluation, enabling viewers to adapt LLMs for specific tasks or domains.

Step 4: Build, Deploy & Operationalize LLM Applications

 
Learning a concept theoretically is one thing; applying it practically is another. The former strengthens your understanding of fundamental ideas, while the latter enables you to translate those concepts into real-world solutions. This section focuses on integrating large language models into projects using popular frameworks, APIs, and best practices for deploying and managing LLMs in production and local environments. By mastering these tools, you'll efficiently build applications, scale deployments, and implement LLMOps strategies for monitoring, optimization, and maintenance.

• Application Development: Learn how to integrate LLMs into user-facing applications or services.
• LangChain: LangChain is the fast and efficient framework for LLM projects. Learn how to build applications using LangChain.
• API Integrations: Explore how to connect various APIs, like OpenAI’s, to add advanced features to your projects.
• Local LLM Deployment: Learn to set up and run LLMs on your local machine.
• LLMOps Practices: Learn the methodologies for deploying, monitoring, and maintaining LLMs in production environments.

Recommended Learning Resources & Projects

Building LLM applications:

• LangChain Crash Course For Beginners | LangChain Tutorial - A practical guide to building applications with LangChain.
• LangChain Master Class 2024 - Covers over 20 real-world use cases for LangChain.(Recommended)
• OpenAI Api Crash Course For Beginners | Financial Data Extraction Tool Using OpenAI API - This tutorial includes step-by-step instructions to use OpenAI API by building a project. (Recommended)
• Build your own LLM chatbot from scratch | End to End Gen AI | End to End LLM | Mistrak 7B LLM - It provides a comprehensive, step-by-step guide to creating a chatbot powered by the Mistral 7B Large Language Model (LLM).
• LLM Course – Build a Semantic Book Recommender (Python, OpenAI, LangChain, Gradio) - A detailed guide to building a semantic book recommender.
• Youtube Free Playlist Consisting of LLM End to End Projects - A free youtube playlist consisting of 20+ LLM end to end projects. (Recommended)

Local LLM Deployment:

• How to Deploy an LLM on Your Own Machine - Deploy a Large Language Model locally, covering setup and integration.
• How to Run Any Open-Source Large Language Model Locally - Set up and run open-source LLMs on your local hardware.
• Foundations of Local Large Language Models - Offered by Duke University, this course teaches you how to set up a local environment to run various LLMs and interact with them via web interfaces and APIs. (Recommended)
• Beginning Llamafile for LLMs - Learn to serve large language models as production-ready web APIs using the llama.cpp framework.
• Containerizing LLM-Powered Apps: Chatbot Deployment – A step-by-step guide to deploying local LLMs with Docker.

Deploying & Managing LLM applications In Production Environments:

• How to deploy LLMs (Large Language Models) as APIs using Hugging Face + AWS - Covers deploying LLMs as APIs in the cloud.
• LLMOps Instructional Video Series - A comprehensive 5-part series with live demonstrations in Azure AI Studio, guiding you through various aspects of LLMOps.
• Large Language Model Operations (LLMOps) Specialization - This coursera specialization offered by Duke university covers deploying, managing, and optimizing LLMs across platforms like Azure, AWS, Databricks, and local infrastructure. (Recommended)
• Simplify LLMOps & Build LLM Pipeline in Minutes - A tutorial teaching how to streamline LLMOps and construct efficient LLM pipelines using the Vext platform.

GitHub Repositories:

• Awesome-LLM: It is a curated collection of papers, frameworks, tools, courses, tutorials, and resources focused on large language models (LLMs), with a special emphasis on ChatGPT.
• Awesome-langchain: This repository is the hub to track initiatives and projects related to LangChain's ecosystem.

Step 5: RAG & Vector Databases

 
Retrieval-Augmented Generation (RAG) is a hybrid approach that combines information retrieval with text generation. Instead of relying only on pre-trained knowledge, RAG retrieves relevant documents from external sources before generating responses. This improves accuracy, reduces hallucinations, and makes models more useful for knowledge-intensive tasks.

• Understand RAG & its Architectures: Standard RAG, Hierarchical RAG, Hybrid RAG etc.
• Vector Databases: Understand how to implement vector databases with RAG. Vector databases store and retrieve information based on semantic meaning rather than exact keyword matches. This makes them ideal for RAG-based applications as these allow for fast and efficient retrieval of relevant documents.
• Retrieval Strategies: Implement dense retrieval, sparse retrieval, and hybrid search for better document matching.
• LlamaIndex & LangChain: Learn how these frameworks facilitate RAG.
• Scaling RAG for Enterprise Applications: Understand distributed retrieval, caching, and latency optimizations for handling large-scale document retrieval.

Recommended Learning Resources & Projects

Basic Foundational courses:

• Vector Database: Faiss - Introduction to Similarity Search - This video covers the basics of FAISS and explains how it improves similarity search.
• Chroma - Vector Database for LLM Applications | OpenAI integration - Learn how ChromaDB can help you manage vector data for retrieval-based applications
• Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer - This Python course teaches you how to use RAG to integrate your own custom data with Large Language Models (LLMs). (Recommended)
• Introduction to LlamaIndex with Python (2024) - Covers how to use LlamaIndex for efficient data retrieval and integration with LLMs in Python.
• Introduction to Retrieval Augmented Generation (RAG) | Coursera - An introduction to RAG, teaching how to integrate external data with LLMs to enhance accuracy and reduce hallucinations. (Recommended)

Advanced RAG Architectures & Implementations:

• Retrieval-Augmented Generation (RAG) Patterns and Best Practices -This video teaches different RAG architectures, patterns, and best practices for optimizing retrieval and generation processes. (Recommended)
• Fundamentals of AI Agents Using RAG and LangChain - Learn advanced RAG techniques, prompt engineering, and the use of LangChain for building AI agents.
• HybridRAG: Ultimate RAG Engine – Knowledge Graphs + Vector Retrieval – YouTube - Explores the integration of knowledge graphs with vector retrieval to create advanced RAG systems. (Recommended)
• Retrieval Augmented Generation LlamaIndex & LangChain Course - This course teaches how to build efficient RAG systems using modern tools, covering vector databases, embeddings, and real-world applications.

Enterprise-Grade RAG & Scaling:

• RAG: Building enterprise ready retrieval-augmented generation applications - YouTube - This YouTube playlist provides a comprehensive guide to building and optimizing RAG systems, covering concepts, architectures, and practical implementations.
• Multimodal RAG using the Vertex AI Gemini API – Coursera - This project-based course teaches how to perform multimodal RAG using Google's Vertex AI Gemini API, focusing on enterprise-level applications. (Recommended)
• Learn Advanced RAG Tricks with Zain – YouTube - A session on advanced RAG techniques for enterprise applications.

Step 6: Optimize LLM Inference

 
Optimizing inference is crucial for making LLM-powered applications efficient, cost-effective, and scalable. This step focuses on techniques to reduce latency, improve response times, and minimize computational overhead.
 

Key Topics

• Model Quantization: Reduce model size and improve speed using techniques like 8-bit and 4-bit quantization (e.g., GPTQ, AWQ).
• Efficient Serving: Deploy models efficiently with frameworks like vLLM, TGI (Text Generation Inference), and DeepSpeed.
• LoRA & QLoRA: Use parameter-efficient fine-tuning methods to enhance model performance without high resource costs.
• Batching & Caching: Optimize API calls and memory usage with batch processing and caching strategies.
• On-Device Inference: Run LLMs on edge devices using tools like GGUF (for llama.cpp) and optimized runtimes like ONNX and TensorRT.

Recommended Learning Resources

• Efficiently Serving LLMs – Coursera - A guided project on optimizing and deploying large language models efficiently for real-world applications.
• Mastering LLM Inference Optimization: From Theory to Cost-Effective Deployment – YouTube - A tutorial discussing the challenges and solutions in LLM inference. It focuses on scalability, performance, and cost management. (Recommended)
• MIT 6.5940 Fall 2024 TinyML and Efficient Deep Learning Computing - It covers model compression, quantization, and optimization techniques to deploy deep learning models efficiently on resource-constrained devices. (Recommended)
• Inference Optimization Tutorial (KDD) – Making Models Run Faster – YouTube - A tutorial from the Amazon AWS team on methods to accelerate LLM runtime performance.
• Large Language Model inference with ONNX Runtime (Kunal Vaishnavi) - A guide on optimizing LLM inference using ONNX Runtime for faster and more efficient execution.
• Run Llama 2 Locally On CPU without GPU GGUF Quantized Models Colab Notebook Demo - A step-by-step tutorial on running LLaMA 2 models locally on a CPU using GGUF quantization.
• Tutorial on LLM Quantization w/ QLoRA, GPTQ and Llamacpp, LLama 2 - Covers various quantization techniques like QLoRA and GPTQ.
• Inference, Serving, PagedAtttention and vLLM - Explains inference optimization techniques, including PagedAttention and vLLM, to speed up LLM serving.

Wrapping Up

 
This guide covers a comprehensive roadmap to learning and mastering LLMs in 2025. I know it might seem overwhelming at first, but trust me — if you follow this step-by-step approach, you'll cover everything in no time. If you have any questions or need more help, do comment.
 
 

Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook "Maximizing Productivity with ChatGPT". As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She's also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.