Ever wondered what type of AI is ChatGPT (by OpenAI)? This LLM is a generative AI chatbot built on a large language model (LLM). It generates responses as sequences of predicted tokens and is fine-tuned for dialogue using reinforcement learning with human feedback, so it can still produce incorrect or misleading outputs.
Try CustomGPT with the 7-day free trial for cited answers.
TL;DR
ChatGPT is a generative AI chatbot powered by a large language model (LLM). It generates responses by predicting likely text (tokens) based on patterns learned during training, then it’s tuned to follow instructions in a dialogue format using human feedback. It’s useful for language tasks, but it can still produce plausible-sounding mistakes, so verify important facts.
Build a source-cited explainer agent from your docs with our no-code builder.
What ChatGPT “Is”
It Is: A Generative AI System Delivered as a Chatbot
NIST defines generative artificial intelligence as AI models that emulate characteristics of input data to generate derived synthetic content (including text).
ChatGPT is a chat interface/product experience built on top of a large language model. OpenAI describes ChatGPT as a system fine-tuned for dialogue and notes it can respond in a conversational way.
It Isn’t: A Search Engine or Guaranteed Fact Source
OpenAI explicitly warns that ChatGPT can be inaccurate, untruthful, or misleading at times.
How ChatGPT Generates Responses
Step 1: It Works in Tokens, Not “Perfect Sentences”
OpenAI explains that text is split into tokens, the model processes tokens, and the response is generated as a sequence of tokens that gets converted back into text.
Implication: The model is optimized to produce likely continuations, not to fetch ground-truth facts.
Step 2: It’s Tuned for Dialogue Using Human Feedback (RLHF)
OpenAI describes ChatGPT as “optimized for dialogue,” and its help center describes the use of reinforcement learning from human feedback (RLHF) to tune behavior for helpfulness.
A foundational RLHF approach uses human-written demonstrations and preference rankings to train a reward model, then optimize the policy.
Implication: RLHF can improve instruction-following and conversational behavior, but it does not guarantee factual correctness.
What ChatGPT Is Good At
ChatGPT is typically effective for:
- Drafting and rewriting text (tone, clarity, structure)
- Summarizing and outlining
- Explaining concepts at different difficulty levels
- Brainstorming options and organizing messy inputs
Safe operating rule: Treat outputs as a first draft or assistant suggestion, then validate anything high-stakes using primary sources.
Where It Can Fail
Common failure modes:
- Confident-sounding errors: plausible text that’s wrong.
- Missing or invented citations: unless you force grounding to sources.
- Overgeneralization: “average” answers when the question needs constraints (jurisdiction, time period, definitions).
Practical fix: Ask for assumptions, request citations, and cross-check key claims.
Build a Citation-Backed Explainer With CustomGPT.ai
If your JTBD is “answer this consistently with citations,” you want a system that is forced to ground responses to approved sources rather than free-form generation.
- Create an agent from a website URL or sitemap.
- Enable citations so answers show supporting sources.
- For higher grounding quality on larger corpora, enable Highest Relevance (re-ranking for retrieved context).
- Test variations (“Is ChatGPT an LLM?”, “What does generative AI mean?”) and tighten the agent’s definition-first instructions.
- Publish the explainer via embedding if users need self-serve access.
- Keep it neutral: force the agent to label facts vs assumptions and to say “I don’t know” when sources don’t support a claim.
Conclusion
ChatGPT is a generative AI chatbot built on an LLM that predicts tokens and is tuned with RLHF for dialogue – useful, but not a guaranteed fact source.
Next step: For grounded, cited answers, use CustomGPT.ai with its 7-day free trial.
FAQ
Is ChatGPT the Same Thing as a Large Language Model (LLM)?
Not exactly. An LLM is the underlying model class that generates text from prompts. ChatGPT is the product/interface that delivers an LLM in a dialogue format with additional tuning and safety behavior. OpenAI describes ChatGPT as optimized for dialogue and cautions outputs can still be inaccurate.
Does RLHF Make ChatGPT Truthful?
RLHF helps steer outputs toward what humans rate as helpful and safer, using demonstrations and preference rankings, but it doesn’t convert the model into a guaranteed fact source. It can still generate misleading text.
Why Can ChatGPT Sound Confident and Still Be Wrong?
Because it generates likely sequences of tokens based on learned patterns, not by checking truth against a database. OpenAI explicitly warns that outputs may be inaccurate or misleading, so you should verify important claims with primary sources.
How Do I Get Citation-Backed Answers Instead of Free-Form Text?
Use a workflow that forces grounding. In CustomGPT.ai, you can enable citations so responses show which source content supported the answer. This reduces “looks-right” text that has no support in your approved materials.
Can I Build a “ChatGPT Basics” Explainer for My Team Using My Own Sources?
Yes. You can create an agent from a website/sitemap or uploaded docs, tune it to give short definition-first answers, enable citations, and embed it where your team works.