I know you guys, like other developers, just pretend to know each and every AI term. And the truth is, it's not your fault. Every few weeks, AI models are getting better, and it's hard to keep up with so many changes.
This also presents a unique challenge that has been haunting many developers since 2022.
Terms like RAG, MCP, AI Agents, and many more can seem scary.
However, you don't need to stress too much. I'm here to simplify each and every AI term for you guys.
Temperature: Determines the randomness and predictability of the output generated by LLMs. A low temperature produces a predictable response, and a high temperature produces a highly varied and creative response.
Context window: Working memory of AI.
RAG(Retrieval Augement Generator): A private AI brain. You give access to your private data without retraining the model, and it produces output based on it.
For ex, you wasted your whole semester enjoying and your exam is just the next day.
You just have one night that can either make you fail or pass your paper.
You uploaded your university notes and past year questions. You tell your RAG application to help you summarise the notes, ask frequently asked questions that appeared previously, and highlight important topics based on them.
It analyses the available material and helps you prioritise what to study. It can also predict questions that might appear in the exam.
MCP(Model Context Protocol): A standardised way to give access to external tools and data sources, helping to make integrations more consistent and reusable.
For ex, imagine you are building a productivity app that lets users manage tasks, schedule meetings, read emails, and track habits. You want the AI assistant to interact with all these features.
Without MCP, you will have a hard time integrating all these external tools, as you need to manually write custom logic for each tool to connect to them. It makes everything a mess.
That's where MCP steps in and standardises the integrations, saving development time, reducing integration complexity, and making it easier to add new capabilities to your AI assistant.
That's it for part 1. This series continues in part 2, so stay tuned.
Thanks for reading :)
Top comments (2)
This is a friendly glossary, and the 'private AI brain' framing for RAG is a good mental model. One thing that would make these terms stick even better: pair each definition with the one question it answers. Temperature answers 'how random should the output be', context window answers 'how much can it remember at once', and RAG answers 'how do I make it use my data'. When a term maps to a question you have actually asked, it stops being jargon and becomes a tool.
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