If I Were Learning Llms In 2026, I’d Start With These Concepts

This happens to me almost every week. Someone reaches out, feeling overwhelmed by the constant flow of new AI papers, frameworks, and buzzwords, and asks where to start. Most want to dive right into building complex, autonomous multi-agent systems before they even know how a language model reads a sentence. If I were learning LLMs in 2026, I wouldn’t just copy and paste code from the latest GitHub repo. I’d focus on building a strong foundation by learning the core concepts in a clear, logical order.

In this article, I’ll share the order I would follow if I were learning today, along with practical lessons I’ve picked up from building AI systems.

Start With These Concepts for Learning LLMs in 2026

If I were learning LLMs in 2026, here’s the order I’d use, plus some practical lessons from my experience building AI systems.

1. The Input Layer: Tokens and Embeddings

You can’t work with Large Language Models effectively unless you know how they process data. When I first started, I thought language models read text word by word, just like people do. But that’s not the case.

Tokens are the basic units that LLMs use. A token isn’t always a whole word; it can be a part of a word, a syllable, or even just one character. When you give a sentence to an LLM, a tokenizer breaks it into these pieces. Knowing how tokens work is important because they affect both the cost of using models and how much text you can process at a time.

After you have tokens, you need embeddings. Models don’t understand text; they understand numbers. Embeddings turn tokens into high-dimensional vectors. Words with similar meanings end up close together in this space. If you’re an engineer, you’ll use embeddings all the time. They’re the foundation of things like semantic search and recommendation systems.

2. The Engine: Transformers and Self-Attention

Once the text is converted into numbers, the model needs to understand context. This is where the Transformer architecture comes in.

Before Transformers, models had trouble remembering information from the start of a long paragraph by the time they got to the end. The big breakthrough was the self-attention mechanism. Attention lets the model look at one token and give different weights to all the other tokens in the sequence, helping it decide which ones matter most. For example, in the sentence “The bank of the river,” attention helps the model know that “bank” means the side of a river, not a financial institution.

You don’t need to build a Transformer from scratch in PyTorch right away. But knowing that attention is really just a fast way to multiply matrices can help you understand how these models create coherent text.

3. The Interface: Prompting and Context Windows

After you understand the architecture, you can start interacting with it. Many people think prompting just means typing questions into a chat box. But in business settings, prompt engineering is like a new kind of natural language programming. You write clear instructions to make sure the model’s output follows a specific format, such as JSON.

As you try different prompts, you’ll soon run into the limits of the context window. The context window is like the model’s short-term memory. Even though context windows in 2026 are much bigger, models still tend to focus on the start and end of a long prompt and often miss information in the middle. Knowing how to organize context well is a skill that sets senior AI engineers apart from beginners.

4. Making it Yours: Inference and Fine-Tuning

When a model generates text, that’s called inference. The model predicts the next most likely token, one after another. If you understand inference settings like temperature (which controls randomness) and Top-K or Top-P sampling, you can decide if the model should be more factual or more creative.

At some point, prompting won’t be enough, and you’ll need to fine-tune the model. Fine-tuning means taking a pre-trained open-source model and updating its internal weights with data that fits your needs. I always tell learners: don’t fine-tune a model just to add new facts. Use fine-tuning to teach it a new behavior or a specific tone of voice.

5. Enterprise-Grade AI: RAG and Function Calling

One of the most important ideas in modern AI engineering is RAG, or Retrieval-Augmented Generation. LLMs sometimes make up information and present it as fact. RAG fixes this by combining embeddings with search. Instead of just using its training data, the model pulls in relevant documents from your database and uses them in the prompt, so it only answers based on the provided text. If you want a job as an AI or ML engineer today, you need to know RAG.

Function calling goes hand in hand with RAG. This is when LLMs move beyond just generating text and start acting as operational engines. You can define functions, like checking a database, getting weather data, or sending an email. The model can tell when it needs outside information and give you the exact details needed to run your code.

6. The Autonomous Future: Reasoning and AI Agents

The last step in learning is moving from single questions to building autonomous systems. Advanced models are now designed for reasoning, often using a chain of thought approach. This means they break down complex, multi-step problems before giving you a final answer.

When you put together reasoning, function calling, memory, and a control loop, you create an AI agent. An agent does more than just answer questions; it takes a big goal, plans the steps, uses tools to get things done, checks its progress, and keeps going until the task is finished.

Your Next Step in Learning LLMs

If you want to learn more about these topics, my book, Hands-on GenAI, LLMs and AI Agents, is a good next step. It explains LLMs, RAG, AI agents, and Generative AI with clear examples and hands-on projects, so you can go from learning the basics to building real applications.

If you’re looking for a structured course on LLM basics, I also recommend Generative AI with Large Language Models by DeepLearning.AI and AWS. It teaches how LLMs work, how they’re trained and fine-tuned, and how to use them to build real Generative AI applications.

The Takeaway

As a mentor, I often see people get stuck in tutorial hell. They watch hours of videos about these concepts but never actually write any code.

If you want real skills, you need to connect theory with practice. Open a Jupyter Notebook. Try generating embeddings. Build a simple RAG pipeline using your own PDF files. See how the model changes when you adjust the temperature setting.

I hope you found this article helpful for getting started with learning LLMs in 2026. If you want more tips on AI and machine learning, feel free to follow me on Instagram.

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