Concepts

Each page explains one mechanism: what it is, how it actually works, and where it breaks. No vendor pitch, no invented benchmarks.

• PROTOCOLVideo

  What Is MCP (Model Context Protocol)?

  MCP (Model Context Protocol) is an open protocol that standardizes how an AI application connects to external tools, data sources, and systems. Rather than writing a custom integration for every tool a model might use, you implement one interface that any compliant client and server can speak.

• PATTERN

  What Is RAG (Retrieval-Augmented Generation)?

  RAG (Retrieval-Augmented Generation) is a pattern where a system pulls relevant documents or passages at query time and drops them into the model's context before it answers, instead of relying only on what the model learned in training.

• MODEL TYPEVideo

  What Is a Reasoning Model?

  A reasoning model is trained to work through a long run of intermediate steps before it gives a final answer, instead of replying straight away. That buys better accuracy on tasks with multi-step logic, and it costs more compute and latency per query.

• ARCHITECTUREVideo

  What Is an Agent Harness?

  An agent harness is the software around a model that turns it into an agent. It runs the loop: call the model, run whatever tool calls the model asks for, feed the results back, repeat until the task is done. The model brings judgment. The harness brings the loop, the tools, and the guardrails.

• FOUNDATIONS

  What Is an LLM (Large Language Model)?

  A large language model (LLM) is a neural network trained to predict the next token in a run of text, given everything before it. That one training objective, run at enormous scale, is what produces behavior that reads like writing, reasoning, and conversation.

• MODEL TYPE

  What Is a Diffusion Model?

  A diffusion model generates images (or video, or audio) by learning to reverse a process that slowly adds noise to data. Trained to undo that noise one step at a time, the same model can start from pure random noise and walk it back into a coherent image.

• TRAINING TECHNIQUE

  What Is a LoRA (Low-Rank Adaptation)?

  LoRA (Low-Rank Adaptation) is a fine-tuning technique that freezes a pretrained model's original weights and trains a small set of new weights alongside them. It gets most of what full fine-tuning gets you for a fraction of the compute and storage.

• TRAINING TECHNIQUE

  What Is Fine-Tuning a Model?

  Fine-tuning takes a pretrained model and keeps training it on a smaller, more specific dataset, updating its weights so its behavior leans toward that new data. It is how a general base model becomes specialized for a task, a tone, or a domain.