🧠What Are LLMs?
LLMs, or Large Language Models, are AI systems trained to understand and generate human-like language. They’re the technology behind tools like ChatGPT, Claude, and Gemini.
These models can:
- Write essays, blog posts, or emails
- Answer questions like a human tutor
- Generate and explain code
- Summarize long documents
- Help you brainstorm new ideas
Think of them as ultra-smart text assistants — but instead of understanding like a human, they work by predicting the most likely words based on what you type.
🧩 But Wait — What is a “Model” in AI?
Great question. If you’re an engineering fresher, here’s how to think about it:
An AI model is a mathematical program trained to identify patterns in data and make predictions based on those patterns.
For example:
- A language model predicts the next word in a sentence.
- A vision model might detect faces or recognize traffic signs.
- A speech model converts audio to text (like voice assistants).
LLMs are language models, but on a much larger scale — they’re trained on billions of sentences and are capable of understanding context, grammar, and tone to generate natural-sounding text.
🛠️ How Do LLMs Work? (Step-by-Step)
Let’s break down the process of how LLMs take your input and generate smart responses — in simple steps.
🔹 Step 1: Tokenization – Breaking Text into Small Parts
When you type something like:
“What is the capital of India?”
The model doesn’t process it as a sentence. It first breaks it down into smaller parts called tokens. These could be full words, word parts, or even punctuation.
For example:
"What"," is"," the"," capital"," of"," India","?"
These tokens are then converted into numbers and passed into the model for processing.
🧠Why it matters: Each LLM has a token limit (like word count). Exceeding that limit can lead to incomplete answers or extra costs in API usage.
🔹 Step 2: Transformer – The Brain of the Model
Most LLMs today use a technology called the Transformer. It’s a type of neural network that understands the meaning of words based on context.
Let’s say you input:
“Ram saw Shyam at the station. He waved.”
Now the model has to figure out who “he” refers to — Ram or Shyam.
That’s where the self-attention mechanism in the Transformer comes into play. It looks at all parts of the input and decides which words are important and how they’re connected.
This ability to understand relationships between words is what makes models like GPT or Claude seem intelligent.
🔹 Step 3: Inference – Predicting the Next Word
Once the model understands your input, it doesn’t “answer” in the traditional sense.
It actually predicts the next most likely token, one by one.
For example:
Input: “The largest planet in our solar system is…”
The model might generate:
“Jupiter”
Not because it truly knows astronomy — but because it has seen that combination many times during training.
It keeps generating tokens until it feels the answer is complete or hits a stop point.
🔹 Step 4: Training – How the Model Learned Everything
LLMs are trained in two main stages:
- Pre-training:
- The model is fed billions of texts from books, websites, papers, etc.
- It learns patterns, grammar, facts, reasoning.
- Fine-tuning:
- The raw model is improved using human feedback or curated data.
- This makes it more helpful, safe, and polite.
- For example, GPT uses Reinforcement Learning from Human Feedback (RLHF). Claude uses Constitutional AI — a method where it follows ethical principles.
📷 Multimodal LLMs – More Than Just Text
Earlier LLMs could only read and respond to text.
But today’s models (like GPT-4o, Gemini, and Claude 3) can also process:
- 🖼️ Images
- 🔊 Audio
- đź“„ PDFs or spreadsheets
- 🎥 Videos (in some advanced versions)
This is called multimodal AI — meaning it can understand multiple types of input and give smart responses using all of them.
Imagine uploading a chart and asking:
“Can you explain this to me like I’m in college?”
And the model gives you a neat, simple explanation.
That’s what prompt engineers are building in 2025 — tools that talk to you like a friend, but powered by massive AI engines.
🔍 GPT vs. Claude vs. Gemini – What’s the Difference?
Let’s quickly introduce the top 3 models in 2025 that you’ll work with as a prompt engineer.
đź”· GPT-4 / GPT-4o by OpenAI
- Found in ChatGPT (Pro)
- Great at writing, coding, summarizing, and problem-solving
- Multimodal: understands text, image, and audio
- Good balance of creativity + logic
đź”· Claude 3 by Anthropic
- Great for long documents (can read 100+ pages!)
- Focuses on safety, ethics, and clear responses
- Ideal for teaching, legal, and research tasks
- Also supports image input
đź”· Gemini 1.5 by Google DeepMind
- Handles text, images, videos, and even spreadsheets
- Connected deeply with Google apps (Docs, Sheets, Gmail)
- Fast and great at visual + structured data interpretation
- Loved by creators, marketers, and analysts
đź’ˇ Why Should a Fresher Care?
If you’re starting your career in AI, prompt engineering is one of the easiest ways to enter the field — no heavy coding required.
But you can’t prompt well unless you know:
- What an LLM is
- How it processes input
- What it can and cannot do
- How different models behave differently
Once you know this, you’ll be able to write prompts that get the best out of the model — whether you’re solving a coding bug, writing a poem, analyzing a spreadsheet, or creating a chatbot.
🧠Summary – How LLMs Work in Simple Words
- Your input text is broken into tokens (small word chunks)
- The transformer reads the context and relationships between words
- The model predicts the next word/token to generate a response
- LLMs are trained on massive data — they don’t think, but they’re great at guessing
- Modern models can also see images, listen to voice, read files — not just text
- As a prompt engineer, your job is to ask the right question, the right way
🎓 Ready for Next Lesson?
➡️ Lesson 3: Prompt Types – Zero-shot, Few-shot, Chain-of-Thought Explained
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