Plexara
Learning / AI Concepts
LLM & Frontier Models
101 - What is a Large Language Model?
LLMs predict the next token from patterns learned across trillions of training examples. What that means, why it produces fluent reasoning, and its limits.
102 - Tokens and your budget
Every LLM interaction is priced and rate-limited in tokens. What a token is, how much text fits a budget, and how to avoid wasting tokens on useful answers.
103 - Context, compression, and memory
The context window is a model's working memory. Each session balances keeping, compressing, or clearing it. Plexara adds enterprise memory on top.
104 - Frontier models, specialized models, and why enterprise AI uses both
Frontier models bring world knowledge; small local embedding models power memory and catalog search. Knowing each role designs AI systems that work.
105 - What is an AI agent?
An AI agent is not a chatbot and not magic. It is a short loop (think, call-tool, observe, think again) on top of a language model. Mental model first.
110 - Is MCP just an API wrapper?
MCP is not a replacement for your APIs and not a thin proxy. It is an application layer on top, like a website is an application layer on top of its APIs.
200 - Plexara MCP
Now that the LLM foundations are in place, move into the Model Context Protocol and how Plexara extends it for enterprise data.