What is GPT? Understanding the Core Technology Behind Generative AI and How Wissly Enhances It

1. What is GPT and How Does It Work?

GPT (Generative Pre-trained Transformer) is a large and powerful language model (LLM) based on deep learning that can generate human-like text. After being pre-trained on massive datasets of text and code, GPT can handle a wide range of natural language processing (NLP) tasks — from dialog generation and document summarization to multilingual translation and code generation.

Its architecture, the Transformer, introduced by Google in 2017, enables GPT to analyze entire sentences simultaneously, not just word by word. This structure dramatically improves context awareness, training efficiency, and overall response quality.

A core feature of the Transformer is the self-attention mechanism, which helps GPT dynamically understand the relationship between words in a sentence. This allows it to generate more relevant and nuanced language, far beyond simple grammar.

2. The Evolution of GPT: From GPT-1 to GPT-5

• GPT-1 (2018): OpenAI’s first model based on Transformer architecture.

• GPT-2 (2019): 150M parameters, improved text fluency. Initially withheld due to concerns about misuse.

• GPT-3 (2020): 175B parameters, enabled few-shot learning, powering applications like ChatGPT.

• GPT-4 (2023): Multimodal capabilities (text + image), improved safety and response precision.

• GPT-5 (2025): Enhanced reasoning, multimodal depth, integrated into Microsoft Copilot, ChatGPT, and more.

3. Why GPT Matters

GPT enables language interaction that mimics human communication. It’s used to automate workflows, assist with content creation, and enhance internal search and summarization. For businesses, GPT is not just a model — it’s a foundation for smarter productivity tools.

Thanks to its ability to be fine-tuned and prompt-engineered, GPT can be customized to specific industries, use cases, and even internal policy.

4. The Limitations of GPT and How Wissly Solves Them

Wissly’s Retrieval-Augmented Generation (RAG) platform solves these issues:

• Retrieves and injects verified internal data into GPT → grounding prevents hallucinations

• Runs fully on-premises or in a private cloud → ensuring compliance and data privacy

• Customizable to fit any document structure or language style

• Integrates with GPT and other LLMs → enabling secure, multimodal generative AI