llms
Progress from zero to frontier with a guided depth ladder.
How LLMs Work — The Plain English Version
Large language models power ChatGPT, Claude, and Gemini. Here's how they actually work — no jargon, no math, just clear explanations.
What is an LLM?
A simple explanation of large language models and what they are actually doing.
How LLMs Work — What It Means for How You Use Them
Understanding how LLMs work under the hood makes you dramatically better at using them. Here's what every professional needs to know.
Context Windows Explained: Why LLMs Forget — And What to Do About It
Everything you send to an LLM fits inside a context window. Learn what that means, why it matters, and practical strategies for working within — and around — these limits.
LLM Agents vs Chatbots — What Actually Changes in Product Design
A practical framework for deciding when a simple chatbot is enough and when you need an agentic architecture.
The Context Length Frontier: What Million-Token Windows Actually Change
Context windows have ballooned from 4K to millions of tokens. Here's what that actually changes for builders — and what it still doesn't solve.
Fine-Tuning vs. Prompting: Which Should You Use?
Before you invest in fine-tuning, make sure you actually need it. This guide breaks down when prompting is enough and when fine-tuning is the right call.
LLM Inference Latency: Why Your App Feels Slow and How to Fix It
LLM quality matters, but latency often determines whether a product feels magical or frustrating. Here's how inference delay really works and how builders should reduce it.
LLM Inference Optimization Playbook for 2026
How teams cut LLM latency and cost without wrecking answer quality: model routing, prompt reduction, caching, batching, and eval-driven tradeoffs.
Temperature, Top-P, and Sampling: The Knobs That Control LLM Creativity
Temperature and sampling parameters control how creative or predictable your LLM's outputs are. Here's what they actually do and how to use them.
How LLMs Work — The Transformer Architecture Explained
A technical deep-dive into transformer architecture, attention mechanisms, training pipelines, and the engineering decisions that make modern LLMs work.
LLM Context Windows Explained: Why Size Isn't Everything
Context windows keep growing, but bigger isn't automatically better. Here's what context windows actually are, how they work, and why the way you use them matters more than their size.
Constitutional AI and Alignment: Teaching LLMs Values
Constitutional AI offers a scalable approach to aligning language models with human values. This guide explains how it works, how it compares to RLHF, and what it means for building trustworthy AI systems.
Model Distillation and Compression: Making LLMs Smaller Without Losing Capability
A deep dive into knowledge distillation, pruning, and compression techniques that shrink large language models while preserving most of their capability — with practical guidance on when to use each approach.
Long-Context LLMs: How Models Actually Use 128K+ Token Windows
Models now accept 128K–2M tokens of context, but do they actually use all of it? This guide covers how long-context retrieval works, where models struggle, and practical strategies for getting reliable results.
LLM Memory and State: How Models Remember (and Forget) Across Conversations
LLMs don't actually remember anything between conversations. Understanding how statelessness, context windows, and external memory systems interact is essential for building reliable AI applications.
LLM Quantization Methods Explained
A practical guide to quantization methods for large language models — from theory to choosing the right approach for your use case.
Reasoning Models: How LLMs Learned to Think Before They Answer
A technical look at reasoning models — the architecture, training, and inference-time compute strategies behind o1-style thinking. What actually happens when an LLM 'thinks'.
LLM Routing: How to Pick the Right Model for Every Request
Not every prompt needs your biggest model. LLM routing lets you dynamically select the right model per request — balancing quality, latency, and cost. Here's how to build a routing layer.
LLM Scaling Laws Explained: Why Bigger Models Aren't Always Better
Scaling laws govern how model performance improves with more data, compute, and parameters. Understanding them explains why the biggest model isn't always the smartest choice.
Speculative Decoding: How LLMs Generate Text Faster Without Losing Quality
Speculative decoding is one of the most important inference optimizations for LLMs. This guide explains how draft-then-verify works, when it helps, and how to implement it.
LLMs and Synthetic Data: Training on Machine-Generated Text
How synthetic data is reshaping LLM training—from generation strategies and quality filtering to the risks of model collapse and best practices for mixing real and synthetic corpora.
LLM Tool Use and Function Calling: Patterns That Actually Work
A practical guide to LLM tool use and function calling — covering schema design, error handling, multi-step orchestration, and the patterns that separate reliable tool-using systems from brittle demos.
How LLMs Work — Open Problems and Frontier Research
The frontier of LLM research: scaling laws, emergent capabilities, mechanistic interpretability, reasoning limitations, and where the field is heading.