What Are GPT-4 Examples and Which Real-World Use Cases Can You Explore?

Updated

GPT-4 examples are documented applications and demos that show how companies and developers put the multimodal GPT-4 model to work. The most useful way to explore them is by job-to-be-done rather than by brand: start from the task you want to improve (coding, legal research, customer support, education, design), then look at which examples in that category solve a similar problem. On gpt4demo.com, examples are organized into collections and categories — the page lists 87 applications across areas such as application security, AR, autonomous AI agents, banking, browsers, chat bots, legaltech, developer tools, education, and design — so you can browse by category or request a new app if your use case isn't covered.

The main types of GPT-4 examples

The showcase clusters into a handful of recurring patterns. Recognizing the pattern helps you judge whether an example is relevant to your work.

Pattern What it looks like Example entries on the site
Chat bots and assistants Conversational interfaces built on GPT-4 or similar models Claude 2, Chatfuel AI, Fin, Poe by Quora
Coding and developer tools Code generation, autocomplete, agentic coding, model playgrounds GitHub Copilot, Repl.it, Auto-GPT, GPT Engineer, GPT-4 Playground, Semantic Kernel, MindsDB
Legal and professional research Document review, legal research assistants Casetext CoCounsel, Harvey, Aline AI
Education and language learning Tutoring, course support, language practice Khan Academy, CheggMate, Coursera, Duolingo Max
Productivity and notes Copilots embedded in office and note tools Microsoft 365 Copilot, OneNote, Mem, Reflect
Search and browsing GPT-4 integrated into search engines and browsers Microsoft Bing, Microsoft Edge
Design and creative Turning sketches into websites, diagramming GPT-4 Sketch to Website, Diagram
Games and experiments Small demos that test model behavior GPT-4 Doom, GPT-4 Pong, GPT-4 Snake Game, rizzGPT

Reusable patterns behind the examples

Rather than copying a specific product, extract the mechanism each example demonstrates. Four patterns recur across the list:

  • Multimodal input. Examples like GPT-4 Sketch to Website show the model taking an image (a sketch) and producing structured output (a website). The reusable idea: feed a non-text artifact and ask for a text or code artifact.
  • Autonomous agents. Auto-GPT, GPT Engineer, and GPT Assistant show the model planning and executing multi-step tasks with tools. The reusable idea: break a goal into steps and let the model call tools between them.
  • Document question-answering. Legal and research tools (CoCounsel, Harvey, Elicit) ground answers in a specific document set. The reusable idea: supply your own corpus and ask questions constrained to it.
  • Code generation and assistance. Copilot and Repl.it show inline suggestions and conversational coding. The reusable idea: pair the model with a code context window and iterate on its output.

How to decide whether an example fits your case

Use the same criteria for any entry you're considering:

  1. Scenario match. Does the example solve a task close to yours, or only share a category label? A legal research assistant is not automatically useful for general document search.
  2. Integration requirements. Note whether the example is a standalone app, a plug-in to an existing tool (e.g., Microsoft 365 Copilot, Salesforce), or a developer framework (Semantic Kernel, MindsDB). Frameworks require engineering effort; embedded copilots require adopting the host product.
  3. Audience. Some entries are clearly enterprise-facing (Morgan Stanley, Nuance in healthcare, Amplemarket for sales); others are consumer or experimental (games, rizzGPT). Match this to your context.
  4. Maturity signals. The site marks some entries as "Upcoming" (Drupal, Material Security, Maybe) with a "Keep me updated" option, which tells you they aren't available to try yet.

Turning an example into a trial plan

Once an example looks relevant, convert it into a small test rather than a broad evaluation:

  • Pick one scenario from the category that matches your task — for instance, document Q&A if you handle contracts, or code assistance if you maintain a codebase.
  • Prepare representative inputs. Use real samples from your work, not toy data, so output quality is meaningful.
  • Define what "good" means before you run it: accuracy on key facts, time saved, or number of edits needed.
  • Compare against your current process, not against perfection. The question is whether the example's approach beats what you do today.
  • Check the constraints the example implies — data you'd need to supply, tools it must connect to, and whether it's a product you can actually access.

If nothing in the 87 listed applications matches your need, the site offers a "Request app" path, which is itself a signal that the showcase is a curated, growing list rather than an exhaustive catalog.