ChatGPT: Complete Definition and Guide

What is ChatGPT?

ChatGPT is a conversational artificial intelligence application created by OpenAI and launched in November 2022. Built on the GPT (Generative Pre-trained Transformer) model family, ChatGPT allows users to converse in natural language with an extremely powerful language model. By reaching 100 million users in two months, ChatGPT became the fastest-growing application in history and triggered the global wave of generative AI adoption.

The product comes in several versions: free ChatGPT (based on GPT-4o mini), ChatGPT Plus (access to GPT-4o and advanced features like web browsing, image generation, and data analysis), and ChatGPT Enterprise/Team for organisations with privacy guarantees and administration features. OpenAI's API, distinct from the ChatGPT application, allows developers to integrate GPT models into their own applications.

For businesses, ChatGPT represents both an opportunity and a potential trap. Its intuitive interface makes it immediately useful for tasks like writing, research, document summarisation, or code generation. However, its intrinsic limitations (hallucinations, lack of proprietary data, confidentiality) mean that for serious business use cases, a custom AI solution often proves more appropriate.

Why ChatGPT Matters

ChatGPT's impact far exceeds its technical capabilities. It has fundamentally changed how the public and business decision-makers perceive AI.

• AI democratisation: before ChatGPT, AI was perceived as complex technology reserved for data scientists. ChatGPT made AI accessible to everyone, creating a baseline expectation that every business must now meet.
• Individual productivity: employees using ChatGPT for writing, research, and analysis save an average of 1-2 hours per day on repetitive tasks.
• Innovation catalyst: ChatGPT's visibility pushed every sector to explore AI, accelerating adoption of AI solutions in domains that hadn't previously considered it.
• Plugin and GPTs ecosystem: the GPT Store and Custom GPTs enable creating specialised assistants without code, opening AI to non-technical profiles.
• Benchmark standard: ChatGPT has become the reference against which every AI solution is compared, establishing high expectations for conversational fluency and response quality.

How It Works

ChatGPT is built on the Transformer architecture, which processes text as tokens (words or word fragments). The GPT-4 model was pre-trained on a massive text corpus from the Internet, books, and other sources, then fine-tuned via RLHF (Reinforcement Learning from Human Feedback) to produce helpful, honest, and harmless responses.

During a conversation, ChatGPT receives the complete dialogue history as context (up to 128K tokens for GPT-4o), analyses the query, and generates a response token by token. Each token is predicted based on all preceding tokens, creating the impression of natural fluency. The model doesn't 'understand' in the human sense: it identifies extremely sophisticated statistical patterns in textual data.

ChatGPT's advanced features include web browsing (access to current information), code interpreter (Python code execution in a sandbox), image generation (via DALL-E), and vision (image analysis). Custom GPTs allow creating specialised assistants by combining custom instructions, knowledge files, and actions (API calls).

Concrete Example

KERN-IT helps its clients use ChatGPT intelligently and, more importantly, move beyond its limitations. A common case: a Brussels-based SME was using ChatGPT to answer customer questions via a Custom GPT fed with product documentation. Results were fine for general questions, but the assistant regularly hallucinated on pricing, timelines, and contractual specifics.

KERN-IT developed a custom solution: an AI assistant based on a RAG architecture (via LangChain) connected directly to the client's product database, CRM, and contract management system. Unlike ChatGPT which 'guesses' from static documents, this assistant queries data in real time and provides sourced, verifiable answers. The error rate dropped from 15% with the Custom GPT to under 2% with the custom solution, while ensuring complete data confidentiality.

Implementation

1. Assess needs: distinguish use cases suited to ChatGPT as-is (writing, brainstorming, general research) from those requiring a custom solution (business data, critical accuracy, confidentiality).
2. Test ChatGPT Enterprise/Team: for organisational adoption, ChatGPT Enterprise offers privacy guarantees, no training on your data, and administration features.
3. Explore Custom GPTs: create specialised assistants for recurring tasks by providing them with instructions and reference documents.
4. Integrate via the API: for business applications, using OpenAI's API (via LangChain or directly) allows integrating GPT-4 into your existing systems with full control.
5. Identify limitations: if accuracy, confidentiality, or real-time data access are critical, consider a custom AI solution combining LLM and proprietary data via RAG.
6. Train teams: invest in prompt engineering training to maximise interaction quality with ChatGPT and any conversational AI solution.

Associated Technologies and Tools

• OpenAI models: GPT-4o, GPT-4o mini, GPT-4 Turbo, o1, o3 for advanced reasoning
• Alternatives: Claude (Anthropic), Gemini (Google), Mistral, LLaMA (Meta) offer comparable capabilities with specific advantages
• Integration: OpenAI API, LangChain, LlamaIndex for programmatic integration into business applications
• RAG: vector databases (pgvector, Pinecone, Chroma) to enrich responses with enterprise data
• Custom GPTs: GPT Builder for creating specialised assistants without code
• Security: ChatGPT Enterprise, Azure OpenAI Service for compliance-constrained deployments

Conclusion

ChatGPT has transformed the perception and adoption of AI at an unprecedented scale. For businesses, it represents an excellent entry point into conversational AI, but its limitations in accuracy, confidentiality, and proprietary data access mean that critical business use cases often require more sophisticated solutions. KERN-IT helps its clients navigate this transition: from using ChatGPT for general tasks to developing custom AI solutions integrated into existing information systems via RAG architectures and AI agents. The goal is not to replace ChatGPT, but to complement it where it reaches its limits.