What is an LLM? 🤔
An LLM (Large Language Model) is a giant neural network — usually a Transformer — that's been trained on HUGE amounts of text from the internet. It learns the patterns of human language well enough to generate new text, answer questions, write code, translate, summarize, and much more.
The name says it all:
- Large — trained on BILLIONS of pages of text; has billions of parameters
- Language — understands the words humans use
- Model — a pattern-finder that predicts what word comes next
How Do LLMs Actually Work? 🔮
Here's the surprising truth: at their core, LLMs do ONE simple thing — predict the next word. That's it!
Imagine I give you the start of a sentence: "The cat sat on the ___". Your brain probably auto-completes it with "mat" or "couch" or "windowsill." LLMs do this exact thing, but for billions of word patterns at once.
To answer "What's 2+2?", the LLM doesn't actually do math. It thinks: "The most likely next words after 'What's 2+2?' are usually '4' or 'It is 4.'" So it predicts those words. Same for chatting, writing code, anything!
Tokens: The Building Blocks 🧱
LLMs don't really see "words" — they see tokens. A token is a small chunk of text. Common words might be one token; rare words get split.
For example, "playing" might be tokens [play] + [ing]. The word "Sulabh" might be [Sul] + [abh]. The model converts every input to tokens, processes them as numbers, then converts the output back to text.
The famous context window measures how many tokens a model can handle at once. GPT-4 supports 128,000 tokens. Claude can handle 200,000+ tokens — that's an entire book!
How Are LLMs Trained? 🎓
Training a modern LLM is a HUGE undertaking. Here's the simplified process:
- Pre-training (huge step): Feed the model TRILLIONS of words from the web, books, Wikipedia, code repositories, etc. The model learns to predict the next token. Costs millions of dollars.
- Fine-tuning: After pre-training, the model is good at predicting text but not necessarily helpful or polite. So engineers train it more on examples of helpful responses.
- RLHF (Reinforcement Learning from Human Feedback): Real humans rate the model's answers (good vs. bad). The model learns from these ratings to be more helpful, honest, and harmless. This is what made ChatGPT feel like a friendly assistant!
The 7 Types of LLMs 🎨
Not all LLMs are the same. Here are the famous types:
1. Base Models
The raw, freshly-pre-trained LLM. Good at completing text but not yet good at following directions. Examples: Llama base, Mistral base.
2. Instruction-Tuned Models
Base models fine-tuned to follow instructions. This is what you usually use! Examples: ChatGPT, Claude, Gemini chat versions.
3. Reasoning Models
Newer models that "think out loud" step-by-step before answering. Better at math, logic, and complex problems. Examples: OpenAI o1/o3, DeepSeek R1.
4. MoE (Mixture of Experts)
Models with many specialist sub-networks. Only relevant ones activate for each query, making them efficient. Examples: Mixtral, DeepSeek V3.
5. Multimodal Models
Handle text PLUS images, audio, or video. Examples: GPT-4o, Claude Opus, Gemini 2.0.
6. Hybrid Models
Switch between fast-mode and deep-thinking-mode based on the task. Examples: Claude Sonnet with extended thinking.
7. Deep Research Agents
LLMs that can browse the web, plan multi-step research, and write structured reports. Examples: ChatGPT Deep Research, Claude Research, Perplexity.
Famous LLMs You Should Know 🌟
- ChatGPT / GPT-4 / GPT-5 — by OpenAI
- Claude (Opus, Sonnet, Haiku) — by Anthropic
- Gemini — by Google
- Llama — by Meta (open-source!)
- Mistral / Mixtral — by Mistral AI (French company)
- Qwen — by Alibaba
- DeepSeek — Chinese company; famous for reasoning models
Open-Source vs Closed-Source LLMs 🔓🔒
LLMs come in two flavors:
- Closed-source: GPT-4, Claude, Gemini. You can use them via API or chat, but can't see inside or download the weights.
- Open-source: Llama, Mistral, Qwen, DeepSeek. You can download the model weights, run them on your own computer, and even modify them!
Open-source LLMs are SUPER important for research and education. You can run smaller versions (like Qwen 4B) on a regular laptop or single-board computer like a Raspberry Pi or Jetson Orin Nano!
What Can LLMs Do? 🚀
LLMs are surprisingly versatile. Here's a sample:
- Answer questions and explain concepts (like a tutor!)
- Write essays, stories, poems, songs
- Write and debug code in dozens of programming languages
- Translate between languages
- Summarize long documents
- Help with brainstorming and creative tasks
- Roleplay characters or scenarios
- Tutor students in math, science, history
- Help write emails, resumes, applications
- Generate test questions and study guides
What LLMs CAN'T Do (Yet) ⚠️
LLMs are amazing but NOT perfect. Some limitations:
- Hallucinations — they confidently make stuff up sometimes. Always verify important facts!
- Math is shaky — they're not calculators. For tricky math, use a real calculator.
- Knowledge cutoff — they only know about events up to their training date.
- Can't truly reason — they predict patterns, not deeply understand.
- Bias risk — they inherit biases from training data.
- Need tools to take action — by themselves, they only generate text. They can't send email, browse the web, or order pizza without help.
Important LLM Vocabulary 📚
- Token — small chunk of text the model processes
- Parameter — internal "dial" the model learns. LLMs have BILLIONS!
- Context window — how many tokens the model can handle at once
- Pre-training — initial training on huge text data
- Fine-tuning — additional training for specific tasks
- RLHF — Reinforcement Learning from Human Feedback
- Hallucination — when an LLM confidently generates false info
- Prompt — the text/question you give the LLM
- Prompt engineering — crafting better prompts to get better answers
- System prompt — instructions the developer sets to guide the LLM's behavior
- Temperature — controls randomness (low = deterministic, high = creative)
- Inference — running a trained model to generate responses
Common Questions & Answers 🎯
Q: What does LLM stand for?
A: Large Language Model.
Q: What's the basic thing an LLM does?
A: Predict the next token (word).
Q: All GPTs are LLMs, but not all LLMs are GPT — true?
A: TRUE. Claude, Gemini, Llama are LLMs but NOT GPT.
Q: When did ChatGPT launch?
A: November 2022.
Q: What architecture do all modern LLMs use?
A: Transformer.
What's Next? 👉
- 👉 What is GPT? — the famous family inside LLMs
- 👉 Generative AI — LLMs are part of GenAI
- 👉 AI Agents — LLMs + tools = autonomous helpers