Large Language Models vs. Generative Apps: Who Builds What?

What is a Large Language Model?

A Large Language Model (LLM) is a type of artificial intelligence trained on enormous amounts of text data to learn patterns in human language. These models are capable of generating text, answering questions, summarizing documents, and more, all by predicting what word should come next in a sequence.

LLMs are not simple programs. They are built using deep learning architectures known as transformers, and their training can take weeks or months using powerful computing hardware and massive datasets.

Some of the most well-known LLMs include:

• GPT-4 from OpenAI
• Claude from Anthropic
• Gemini from Google
• LLaMA 2 from Meta (open-source)

These models are typically trained once and then offered through cloud-based APIs so that developers around the world can use them without needing to understand or recreate the training process.

Who Builds Large Language Models?

Developing an LLM from scratch is a complex task that typically requires:

• AI researchers who design the architecture and oversee the training.
• Data engineers who prepare and manage massive datasets.
• Machine learning engineers who handle model training and optimization.
• Infrastructure specialists who run the training jobs on powerful computing clusters.

This work is usually done at large tech companies, academic research institutions, or AI startups with significant funding.

In short: Building an LLM is expensive, difficult, and time-consuming. It is a job for experts in AI development, not something most developers need to do themselves.

What are Generative AI Apps?

Generative AI apps are applications built on top of LLMs. These apps do not involve training a model from scratch. Instead, they connect to existing models using API endpoints, and they focus on:

• Crafting effective prompts
• Designing user interfaces
• Processing and formatting inputs and outputs
• Creating specific workflows (such as writing assistants or customer support tools)

Examples of Generative AI apps include:

• A tool that rewrites emails based on your tone
• A chatbot that explains legal documents
• A summarizer for meeting transcripts
• A resume editor with built-in suggestions

These apps are often built by:

• Developers who integrate the AI model into a working application
• Prompt engineers who fine-tune prompts to improve outputs
• Designers and product managers who shape how users interact with the app

Key Differences Between LLMs and GenAI Apps

Why Most Developers Build on Existing Models

Today, almost every GenAI application you use from ChatGPT to Notion AI to Google’s Help Me Write is built on top of an existing LLM.

Why? Because:

• The hard part is already done by model creators.
• You can use cloud platforms like OpenAI, Google, and Amazon to access these models through simple APIs.
• It is much faster and more cost-effective to build something useful using an API than to train your own model.

Summary – Key Takeaways

• LLM development is done by researchers and data scientists at large companies or research labs.
• Generative AI apps are built by developers who connect to these models using APIs.
• Most developers do not build models from scratch , they use and customize existing ones.
• Knowing how to use and prompt these models effectively is a valuable skill.