Controlling LLM Responses
By now, this series has covered a lot of individual levers — temperature, top-p, system prompts, structured formatting, JSON output. "Controlling LLM responses" is really the umbrella topic tying all of that together: the full toolkit available for shaping what a language...
JSON Output from LLMs
Ask a language model a question in a chat app, and a flowing paragraph of text is exactly what you want. But ask that same model to power a piece of software — feeding its answer into a database, a UI component, or another system — and free-flowing prose becomes a liability …
Structured Prompting
Not every prompt has to be a flowing paragraph of natural language. Sometimes the most effective way to communicate with a language model is to organize your request into clear sections, labels, and formatting — almost like filling out a form rather than writing an essay....
Prompt Templates
If you've ever found yourself typing a similar prompt over and over — just swapping out a name, a topic, or a few details each time — you've already stumbled onto the core idea behind prompt templates. Instead of reinventing a well-crafted prompt from scratch every time, you...
System Prompts vs User Prompts
Every message you send to a chatbot is only part of the full picture. Behind the scenes, there's usually another layer of instructions — invisible to you — quietly shaping how the model behaves before your message even arrives. That hidden layer is the system prompt, and...
Role-Based Prompting
"You are an experienced divorce lawyer." "You are a patient, encouraging math tutor for a 10-year-old." "You are a skeptical peer reviewer examining this research paper." These short framing statements — assigning a language model a specific role or persona — can noticeably...
Chain-of-Thought Prompting
Ask an LLM a multi-step math problem and demand an immediate answer, and it might get it wrong — even if it "knows" all the individual facts needed to solve it. Ask the same question but add "think step by step," and accuracy often jumps dramatically. That gap is what...
Few-Shot Prompting Explained
Sometimes the clearest way to tell a model what you want isn't to explain it — it's to show it. That's the entire idea behind few-shot prompting: instead of describing a task in the abstract, you give the model a small number of worked examples directly in the prompt, and let …
Zero-Shot Prompting Explained
Not every prompt needs examples, instructions on tone, or a step-by-step breakdown to get a useful answer. Sometimes you can just ask a question directly — no setup, no demonstration — and the model still gets it right. That's zero-shot prompting, and it's one of the most...
What is Prompt Engineering?
Ask ChatGPT the same underlying question two different ways, and you can get wildly different quality answers. That gap — between a mediocre response and a genuinely useful one — is often just a matter of how the prompt was written. That skill has a name: prompt engineering....
Self-Attention in Transformers
Self-attention is the mechanism that made transformers possible — and by extension, made models like GPT and Claude possible. It's been touched on in a few earlier posts in this series, but it deserves its own deep dive, since it's genuinely the core computational idea the...
Attention Mechanism Explained Simply
"Attention" is the single most important idea behind modern AI language models — so important that the paper that introduced transformers was literally titled "Attention Is All You Need." But the term itself can feel abstract. What does it actually mean for a computer program...
Transformer Architecture for Beginners
If there's one invention responsible for the current AI boom, it's the transformer. Introduced in a 2017 research paper titled "Attention Is All You Need," the transformer architecture is the foundation behind virtually every major LLM today — GPT, Claude, Gemini, Llama, all...
How LLMs Understand Natural Language
Language is messy. The same sentence can mean different things depending on context, tone, or who's speaking. Words have multiple meanings, sarcasm flips a sentence's intent entirely, and pronouns can point to almost anything nearby. Yet LLMs handle this messiness remarkably...
My GenAI Blogs
Why GenAI? Generative AI has completely changed how I think about software, creativity, and problem-solving. Over the past year, I've gone deep into the world of large language models, prompt engineering, retrieval-augmented generation, fine-tuning, and AI agents. The pace of...
What Makes an LLM "Intelligent"?
Large language models can pass bar exams, write working code, debug their own mistakes, and explain complex topics in plain English. It's natural to call that "intelligent." But is it the same kind of intelligence humans have — or something else entirely wearing a very...
Foundation Models Explained
You've probably heard GPT-4, Claude, Gemini, and Llama described as "foundation models." It's become one of the defining terms of the current AI era — but what actually makes a model a "foundation" model, rather than just... a model? Here's what the term means and why it...
Training vs Inference in LLMs
Every large language model has two very different lives: the phase where it learns, and the phase where it works. These are called training and inference, and while they're related, they're almost opposite in how they operate, what they cost, and what they're trying to...
What is an AI Inference?
You'll often hear phrases like "inference costs," "running inference," or "inference speed" when people talk about deploying AI models. It's one of those terms that sounds technical but describes something fairly simple: the moment an AI model actually gets used to produce an...
Why LLMs Hallucinate
Ask an AI model a question and it might give you a confident, well-written, completely wrong answer — a fabricated statistic, a made-up book title, a court case that never existed. This phenomenon is called "hallucination," and it's one of the most important limitations to...