LLM guide

How to learn LLMs.

For most people, "learning LLMs" means learning to use them well — not building them. A large language model (LLM) like ChatGPT, Claude, Gemini, or Perplexity is an AI trained on huge amounts of text to understand and generate language. To get good at using them, practice prompting daily, learn a handful of core concepts, and build the judgment to know when to trust the output. Here's how.

ChatGPTClaudeGeminiPerplexity

Test your LLM skills free Get Iro AI free

What is an LLM, in plain English?

A large language model (LLM) is an AI system trained on enormous amounts of text to predict the most likely next word. That simple mechanism, at huge scale, lets it answer questions, write, summarize, translate, code, and reason in natural language. When you chat with ChatGPT, Claude, Gemini, or Perplexity, you are using an LLM. You don't need to understand the math to use one well — just as you don't need to understand engines to drive.

Two ways to "learn LLMs" — pick yours

Be clear about your goal, because the paths are very different:

Learn to use LLMs (most people): a practical, language-and-judgment skill. No coding or math required. This is what gets you better results at work and in daily life — and it's what this guide focuses on.
Learn to build LLMs (engineers/researchers): a technical path covering Python, linear algebra, probability, neural networks, transformers, and training. Pursue this through machine-learning courses if you want to create or fine-tune models.

How to get good at using LLMs

Pick one model and use it daily. ChatGPT is the easiest starting point. Use it on real tasks so the practice sticks.
Prompt deliberately. Give context, assign a role, state clear instructions, and specify the output format — then iterate on the first answer. This is the skill that transfers to every model.
Learn the core concepts (below) so the model's behavior stops feeling random.
Verify and build judgment. LLMs can be confidently wrong (a "hallucination"). Always sanity-check anything important.
Branch out. Once one model feels natural, add Claude, Gemini, and Perplexity for their strengths.

Key LLM concepts to understand

Concept	What it means	Why it matters
Tokens	The chunks of text a model reads and writes (roughly ¾ of a word each)	They determine length limits and cost
Context window	How much text the model can "see" at once	Long documents may need summarizing or chunking
Prompt	The instructions and context you give the model	Better prompts = better answers; this is the main lever you control
Hallucination	A confident but false or made-up answer	Why you must verify facts, citations, and numbers
Temperature	How random vs. focused the output is	Lower for facts and code, higher for brainstorming
System prompt	Background instructions that shape every reply	Sets the model's role, tone, and rules
Grounding / RAG	Connecting the model to real sources	Reduces hallucinations and adds citations (e.g. Perplexity)

These terms — and many more — are defined plainly in the Iro AI glossary.

Which LLM should you learn first?

Start with ChatGPT — the most versatile, with a generous free tier. Then expand based on what you do: Claude for long-form writing and code, Gemini if you live in Google apps, and Perplexity when you need answers with citations. Because prompting skills transfer, getting fluent with one model makes the rest easy. See the full AI tools comparison for a side-by-side.

The fastest way to learn to use LLMs

Iro AI is a free iPhone app built to make you fluent with LLMs through five-minute daily lessons — often called the Duolingo for AI. You practice real prompts in the Prompt Lab and get instant feedback, learn the concepts above in plain language, and keep a streak as you climb six ranks. It covers ChatGPT, Claude, Gemini, Perplexity, prompt engineering, AI agents, and more across 18 learning paths. Start with the free AI IQ test to see how good your LLM skills already are.

Start learning LLMs free How to learn AI

Questions people ask

What is the best way to learn LLMs?

For most people, the best way to learn LLMs is to learn to use them well: practice prompting a model like ChatGPT, Claude, or Gemini every day, and learn the core concepts (tokens, context windows, hallucinations, and temperature) as you go. Iro AI turns this into short, gamified daily lessons on iPhone. Learning to build LLMs is a separate, more technical path that needs machine-learning study.

What is an LLM in simple terms?

A large language model (LLM) is an AI system trained on huge amounts of text to predict the next word, which lets it answer questions, write, summarize, and reason in natural language. ChatGPT, Claude, Gemini, and Perplexity are all powered by LLMs.

How do I get good at using LLMs like ChatGPT and Claude?

Practice prompting deliberately: give context, assign a role, state clear instructions, specify the output format, and iterate on the first answer. Learn to spot hallucinations and verify important claims. Daily reps on real tasks build the skill faster than watching tutorials. Iro AI's Prompt Lab gives you feedback on real prompts.

Do I need to code or know math to learn LLMs?

No — not to use them. Using LLMs effectively is a language-and-judgment skill, not a coding one. Math and coding only matter if you want to build, train, or fine-tune models yourself.

Which LLM should I learn first?

Start with ChatGPT — it is the most versatile and has a generous free tier. Once you are comfortable, try Claude for writing and code, Gemini inside Google apps, and Perplexity for cited research. The prompting skills transfer across all of them.