Learn Articles
LLM Sampling Parameters Explained: Temperature, Top-p, and Friends
What temperature, top_p, top_k, penalties, and seeds actually do to LLM output, which defaults to trust, and which knobs deserve adjustment for each task.
Fine-Tuning vs RAG vs Prompt Engineering: Which One Fixes Your Problem
The three ways to adapt an LLM solve different failure modes. A decision framework for choosing between prompting, retrieval, and fine-tuning, with costs.
What Is an LLM API? How Applications Talk to Language Models
How LLM APIs work: the request and response anatomy, tokens as the billing unit, streaming, API keys, and why one wire format became the standard.
Instruct vs Thinking vs Base: What Model Suffixes Mean
The same model ships as base, Instruct, and Thinking, and the suffix changes what it can do more than the parameter count does. What each stage means and which one fits each workload.
What Are Embeddings? How Text Becomes Vectors of Meaning
Text embeddings turn words and documents into vectors so meaning can be measured by distance. How they are produced, what they power, and where they fall short.
What Is Tokenization? How Language Models Read Text
Before a model can read or write a single word, text is broken into tokens. How tokenization works, why subword units won, and why tokens decide both cost and context limits.
What Is Model Routing? Matching Every Request to the Right Model
What model routing is and how it works: sending each request to the best LLM on quality, cost, and latency, and what separates real routing from fallbacks.
What Is a Context Window? How LLM Context Limits Work and Why the Headline Number Misleads
What an LLM context window is, how tokens are counted against it, why bigger windows cost more, and how to work within limits without losing quality.
What Is Retrieval-Augmented Generation? How RAG Works and Why Most Production LLM Apps Use It
How retrieval-augmented generation grounds LLM answers in your own data: the pipeline, chunking and embedding choices, and where RAG beats fine-tuning.
What Are Small Language Models? Where the Sub-10B Tier Earns Its Keep and Where It Breaks
What small language models are, why 1-10B parameter open-weight models now power a large share of production AI, and when to choose one over a frontier model.
What Is Tool Calling? How LLMs Invoke External Functions and Why Agents Depend On It
How tool calling lets LLMs invoke functions and APIs: the request-response loop, schema design, common failure modes, and how to build reliable agents on it.
What Are Reasoning Models? How Test-Time Compute Works and Why It Costs More
What reasoning models are, how chain-of-thought at inference time changes quality and cost, and when o-series or R1-style models are worth the extra tokens.
AI Inference vs Training: The Technical and Economic Differences
The difference between AI training and inference: what each phase does, why inference dominates production cost, and what that means for infrastructure.
What is AI Inference? A Complete Guide
AI inference is the runtime phase where a trained model produces outputs from new inputs. It is the layer that dominates the cost, latency, and reliability of every AI-powered product in production.
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