#prompt-engineering
Tokens, Context Windows & Model Parameters
When you work with LLM APIs, three concepts come up constantly: tokens, context windows, and model parameters like temperature. These aren’t abstract theory — they directly affect your costs, the quality of responses, and what you can build. This tutorial covers all three with practical examples. If you haven’t already, read How LLMs Work first for the underlying architecture. Tokens LLMs don’t read text the way humans do. They break input into tokens — chunks that are roughly word fragments. Read more →
March 28, 2026
Fine-Tuning vs RAG vs Prompt Engineering
You’ve built a prototype with an LLM and it works pretty well, but the model doesn’t know about your company’s products, it sometimes gets the tone wrong, or it hallucinates facts about your domain. How do you fix that? There are three main approaches to customizing LLM behavior: prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning. Each solves different problems, and choosing the wrong one wastes time and money. This tutorial breaks down when to use each. Read more →
March 28, 2026
Prompt Engineering Fundamentals
Prompt engineering is the practice of crafting inputs to get better outputs from LLMs. It’s not magic — it’s about understanding how these models process text and structuring your requests accordingly. A well-written prompt can be the difference between a vague, unhelpful response and exactly what you need. This tutorial covers the foundational techniques. If you’re new to GenAI, start with What is Generative AI? and How LLMs Work first — understanding how models predict tokens will make these techniques more intuitive. Read more →
March 28, 2026
Zero-Shot, Few-Shot & Chain-of-Thought Prompting
In Prompt Engineering Fundamentals, we covered the basics of writing effective prompts. Now let’s look at three specific techniques that can dramatically improve the quality and accuracy of LLM responses: zero-shot, few-shot, and chain-of-thought prompting. These aren’t just academic concepts — they’re practical tools you’ll use every day when working with LLMs, whether you’re building applications or just getting better answers from a chat interface. Zero-Shot Prompting Zero-shot means asking the model to perform a task without giving it any examples. Read more →
March 28, 2026
System Prompts & Role Design
When you build an application on top of an LLM, you don’t want to repeat the same instructions in every user message. That’s what system prompts are for. A system prompt is a set of instructions that defines how the model should behave across an entire conversation — its role, tone, constraints, and output format. This tutorial covers how system prompts work, how to design effective ones, and common patterns you’ll use when building LLM-powered applications. Read more →
March 28, 2026
Structured Output & JSON Mode
When you’re building applications with LLMs, you usually need the output in a specific format — JSON, CSV, a particular schema. But LLMs are text generators by nature, and they don’t always cooperate. They might add explanatory text around your JSON, produce invalid syntax, or miss required fields. This tutorial covers the techniques and tools for getting reliable structured output from LLMs. You should be familiar with Prompt Engineering Fundamentals and System Prompts & Role Design before reading this. Read more →
March 28, 2026