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GenAi Jargon for Non-Technical Founders

· Atharv Parlikar

A practical guide to understanding the terminology without the confusion.

If you’re building anything in AI, you’ll inevitably use terms like model, training, fine-tuning, and pre-trained. The goal isn’t to become an ML engineer, it’s to understand these concepts well enough to communicate clearly and make informed decisions.

Let’s start from the bottom.

What is a model, really?

At the deepest level, a modern AI model is just a huge pile of numbers. Those numbers are called weights.

You can think of a model like a very large function. You give it input, text, images, or audio, and it produces output. The way it turns input into output is completely determined by those weights.

At the beginning, those numbers are random. The model is useless. It gives nonsense. Then comes training.

Training is the process of slowly adjusting those numbers so that the model’s outputs become more and more correct or useful. For a language model, that means predicting the next word well. For an image model, that means generating visuals that match descriptions. For a video model, it means producing frames that are temporally consistent and realistic.

Training is expensive. Very expensive. This is why companies like OpenAI, Google, and Anthropic matter. They’re not just selling APIs, they own and operate massive training pipelines that turn random numbers into powerful models. Those trained weights are the real asset.

What does “pre-trained” actually mean?

In real AI terminology, pre-training means the big, expensive, initial training run where a model learns from huge amounts of data. It’s the phase where GPT, Claude, DALL-E, Stable Diffusion, or any other foundation model is created.

So when a startup says “we use a pre-trained model,” what they usually mean is “we call OpenAI, Anthropic, Midjourney, or some other provider’s API.”

That’s not wrong, but it’s not particularly meaningful either. Every AI product using GPT, Claude, Midjourney, or Runway is using pre-trained models. It’s like saying “we use electricity”, true, but not a differentiator. What matters is what you build on top of it.

Training vs Inference

There are two very different things that get mixed up all the time:

When you send a prompt to an API and get an answer back, you’re doing inference. When you generate an image, edit a video, or transcribe audio, that’s all inference. You’re not training anything. You’re just running your input through a fixed set of numbers that someone else trained.

This distinction matters because it defines who owns what. If you’re only doing inference, you don’t own the model, you’re renting intelligence. And that’s fine. Most successful AI startups today do exactly that. But you should be clear about it.

What does fine-tuning really mean?

Fine-tuning has a very specific technical meaning. It means taking an already trained model and doing additional training on it so that the weights themselves change to better match your use case.

Here’s the critical part: if you don’t have access to the weights, you cannot do real fine-tuning.

Most closed models today don’t give you their raw weights. At best, they let you upload examples or preferences, which influence how the model behaves, but the underlying numbers are still controlled by them. That’s not the same thing as fine-tuning in the original sense, it’s closer to instruction tuning or style guidance.

So if you say “we fine-tuned GPT,” what you’re really claiming is that you were able to change OpenAI’s core model. In almost all cases, that’s not true. What you probably did was give it better prompts, examples, memory, or retrieval. The same applies to image and video models, using ControlNet, LoRAs, or style references isn’t fine-tuning the base model. That’s powerful and valuable work, but it’s not the same thing.

True fine-tuning requires:

Where open source fits in

With open-source models, things are different. You can download the weights. You can run them. You can fine-tune them. You can change the numbers.

This is why open models matter, and why some teams invest in them. But it’s also expensive. You need GPUs, infrastructure, and expertise. Training and fine-tuning aren’t free just because the model is open.

So again, it’s about being honest about where you sit on the stack.

Context: The Model’s Working Memory

When you send a prompt to an AI model, the model doesn’t actually “remember” your previous conversations or have access to external information. Everything it needs to work with must be included in what you send it. This is called context.

Think of context as the model’s working memory for that specific request. It includes:

The model can only “see” what’s in the context. If you want it to know about your company’s specific data, your previous conversation, reference footage, or any external information, you have to put it there.

Context Window

Every model has a context window, a hard limit on how much information it can process at once. For text models, this is measured in tokens (roughly, words or word fragments). For multimodal models, images and video frames also consume tokens.

For example:

This matters because:

The context window is both powerful and limiting. You can include entire documents or multiple reference images, but you’re still constrained by that ceiling.

RAG: Retrieval-Augmented Generation

So you want your AI to know about your company’s video library, brand guidelines, or product images. But you can’t fit everything into the context window every time. This is where RAG comes in.

RAG works by storing your data in a searchable format, then retrieving only the most relevant pieces when needed and inserting them into the context along with the user’s input. Instead of fine-tuning, you’re dynamically pulling in the right information at inference time.

This is the most common architecture pattern for AI products because it’s cheaper than fine-tuning, easier to update, and works with closed models via API. It’s particularly useful for company-specific knowledge, reference footage, image libraries, and any data that changes frequently.

Context Engineering (aka Prompt Engineering)

Now that you understand context and RAG, you can see why context engineering (often called prompt engineering) is real technical work, not just “writing good prompts.”

Context engineering is about:

This is where most AI startups actually spend their technical effort, and it’s legitimate differentiation. Two products using the same model can perform vastly differently based on how well they engineer their context.

The AI stack, simplified

You can think of the modern GenAI world as three layers:

At the bottom are the foundation models. This is where weights are trained. This is where OpenAI, Google, and Anthropic live. This layer requires millions of dollars, thousands of GPUs, and specialized expertise.

In the middle is the API and inference layer. This is where you send prompts and get outputs. This is where most startups plug in. You’re using trained models but not changing them.

At the top is the product layer. This is where memory, prompts, workflows, brand rules, retrieval systems, video processing pipelines, image generation workflows, and user experience live. This is where your actual product differentiation happens.

Most startups live at the top and the middle. Very few live at the bottom. That’s not a weakness, it’s just reality. Your differentiation usually comes from how you structure tasks, data, and user experience, not from owning a giant model.

How to talk about your product clearly

The goal is to be specific about your actual architecture rather than hiding behind jargon.

Instead of saying:

Try something like:

This tells the truth. It shows you know where your value is. And it doesn’t pretend you own something you don’t.

People who understand AI will respect the clarity. People who don’t will still understand what you mean.

The real takeaway

AI isn’t magic. It’s numbers that got trained at massive scale.

If you don’t own the numbers, you don’t own the model. You own how you use it. And that’s totally fine, as long as you’re clear about it.

Understanding this stack helps you communicate better, make smarter technical decisions, and focus your efforts where they actually create value. You don’t need to own the foundation models to build valuable AI products, most successful AI companies don’t.

What matters is being clear about your architecture and honest about where your differentiation lives. Whether it’s in your prompts, your data pipeline, your UX, or your workflow automation, own it clearly.