ChatGPT (OpenAI GPT models)

ChatGPT is OpenAI’s conversational assistant, launched in November 2022. GPT-4, released in March 2023, was the model family behind its most capable tier for the next two years. Both are large language models trained to generate text, which makes them excellent at transforming content and a poor fit for precise, repeatable text analysis.

How it works

GPT models are transformer networks that predict the next token in a sequence. They are pre-trained on large amounts of public and licensed text, then tuned to follow instructions, including with reinforcement learning from human feedback, as described in the GPT-4 technical report. Each reply is sampled, so the same prompt can produce different answers. The consumer app chooses the model and settings for you; the API lets you pin a model version, lower the temperature and request structured output against a JSON schema.

The names move quickly. OpenAI retired GPT-4 from ChatGPT on 30 April 2025, replacing it with GPT-4o, and launched GPT-5 in August 2025. Whenever you report results, record the exact model and date.

How it’s used in SEO and marketing

• Content transformation: summarising, rewriting, drafting titles and meta descriptions for human review.
• Classification with structured outputs, such as labelling SERP results or intent, when you supply a fixed list of labels and validate the results.
• Brainstorming query variants, FAQs and content angles.
• Monitoring: ChatGPT is now a discovery surface in its own right, so teams track whether and how their brand appears in its answers.

Example

In the MLforSEO entity extraction benchmark, ChatGPT (o1) returned 14 entities in three types and found all 13 core named entities in the test text, with every item appearing verbatim. It was the most conservative tool tested: precise on names, but with no concepts, no salience, no confidence scores and no knowledge-base links. Another chatbot in the same test returned a different list when the identical prompt was run again, which is the repeatability problem in miniature.

Limitations and things to watch

• Non-determinism: research cited on MLforSEO found accuracy can swing by up to 15% between runs even with settings meant to be deterministic.
• No entity database inside the model, so it cannot give you Knowledge Graph IDs and may add plausible entities that are not in the text.
• It processes one prompt at a time in the app, which is slow and costly for site-wide analysis compared with a purpose-built API.
• Model changes can alter results without warning, so a workflow built on one version needs re-testing.
• Check what data you paste in, particularly client or customer information.

If you do use it for extraction, give it a fixed list of types, ask for text that appears verbatim only, and verify the output.

Learn it on MLforSEO

• Comparing NLP APIs for Entity and Sentiment Analysis (vs Generative AI)
• How to Automatically Generate Meta Descriptions with Python: From BERT to LLMs
• How to Automatically Optimize your SEO Metadata with FuzzyWuzzy and OpenAI
• 10 Ways to Automate SERP Analysis with AI
• AI Search Optimization & Agentic SEO (course)