LLM & AI Term Glossary (Level: My Nephew)

@SudhirNakka07|September 8, 2025 (4w ago)5 views

Explaining AI and LLM terms to my 14‑year‑old nephew

A few weekends ago my nephew asked, “So… what do you actually build?” Colleagues hear “AI platform architect” and nod; a teenager hears it and blinks. I ended up sketching boxes and arrows on a napkin and writing down the terms that kept coming up. I cleaned up those notes and turned them into this page.

If you work in another field and just want the gist, this is for you.

Story time: Pizza to the rescue

• Gather Evidence Imagine you're a detective trying to solve the mystery of "What makes a good pizza?" You start by collecting evidence. Hundreds of pizza reviews, ratings, and details about toppings, crust thickness, and cooking time. This is your data.

• Manual Counting: (Statistical Analysis) Now you have to figure out what's happening. Let's look at our data and figure out what makes a good pizza. You count how many people loved thin crust vs. thick crust, calculate average ratings for different toppings, and spot patterns. "Aha! 78% of 5-star reviews mention 'crispy crust' and 'fresh basil.'"

• Automated Pattern Finding: (Machine Learning) While doing the analysis manually works, it's tedious and hard to keep up with the ever-changing pizza market. So, you build a system to automate the process by looking at the data and finding patterns. You feed all this data to this program and say, "Figure out the patterns for me." The program learns that certain combinations (thin crust + fresh mozzarella + basil) tend to get high ratings, while others (thick crust + pineapple + anchovies) are more divisive.

• Predictive Reasoning: (Artificial Intelligence) Now that you already know what makes a good pizza, you can use build a clever system which can help people while making a choice of pizza by making smart decisions. Your system doesn't just find patterns—it gets smart enough to predict. When someone asks, "Should I add mushrooms to my margherita?" it can reason through what it learned and give you a thoughtful answer, maybe even explaining why.

The cool part? This same approach works whether you're predicting pizza ratings, diagnosing diseases, or teaching a car to drive. You collect data, find patterns, and build systems that can make smart decisions.

A Pocket Glossary

Let's check some Short definitions, with tiny examples.

Core ideas

• Artificial Intelligence (AI): Umbrella term for getting computers to do things we call “smart”: decide, reason, plan, or understand.
• Machine learning (ML): Instead of hand‑coding rules, we let programs learn patterns from data. Three common ways:
  • Supervised learning: We give examples with answers (labeled data) and the model learns to map input -> output. Eg: email -> “spam” or “not spam.”
  • Unsupervised learning: No answers provided; the model groups or discovers structure on its own. Think clustering similar songs.
  • Reinforcement learning: An “agent” tries actions, gets rewards or penalties, and learns by trial and error—like teaching a robot to walk.
• Deep learning: Uses layered neural networks to learn complex patterns. This powers modern vision models and LLMs.
• Neural network: Lots of simple units (“neurons”) connected by weighted links. During learning, weights change to improve results.
• Natural language processing (NLP): Getting computers to work with human language—read it, summarize it, translate it, generate it.

LLM‑specific terms

• Large language model (LLM): A model trained on huge text collections to predict the next token. Because that simple goal captures a lot of structure in language, LLMs can answer questions, summarize, write code, and more.
• Generative AI: Systems that create new content (text, images, audio, code) based on patterns they’ve learned.
• Transformer: The neural‑network architecture behind most state‑of‑the‑art language models (e.g., GPT, BERT). Its secret sauce is attention.
• Self‑attention: Lets the model look at all the words in a sequence and decide which ones matter for each position. In “The dog, which was lazy, slept,” attention helps link “was lazy” to “dog.”
• Token: The model’s basic unit of text - often a word piece or punctuation. Breaking text into tokens is tokenization.
• Context window: How many tokens the model can consider at once. Bigger windows mean it can “keep more in mind.”
• Parameters: The internal numbers (weights) the model tunes during training. Modern LLMs have billions or more.
• Hallucination: When a model produces something that sounds confident but is wrong or made up - especially outside its knowledge or when the prompt is vague.

Training and shipping

• Training: Feeding data through a model many times so its parameters adjust and performance improves.
• Fine‑tuning: Starting from a pre‑trained model and specializing it on a narrower task or domain (e.g., a support bot for one company).
• Prompt: The text you give the model as input.
• Prompt engineering: Writing prompts and context in a way that reliably gets the behavior you want.
• Retrieval‑augmented generation (RAG): Before the model writes, fetch relevant facts from a knowledge base (docs, database, etc.) and include them in the prompt. This grounds the output and reduces hallucinations.

That’s it. If you noticed something unclear or missing, ping me and I’ll keep the cheat sheet honest.