3. Training: Pre-training then fine-tuning

We normally think of training as two major phases: pre-training and fine-tuning (plus emerging methods like instruction-tuning, RLHF).

Pre-training:

• The model is fed huge corpora of text from the internet: encyclopedias, code repositories, books, websites. For example, the AWS page states that some LLMs ingest data from Common Crawl (50 billion+ web pages) and Wikipedia (~57 million pages) during pre-training.

• The objective in pre-training is often a language modelling one: given context, predict the next token. Formally: $P(\text{next token}\mid \text{context})$ is maximised.

• This enables the model to learn general structure of language, grammar, semantics, common knowledge.

Fine-tuning plus instruction-tuning:

• After pre-training, models are often fine-tuned on narrower datasets or on tasks like answering questions, dialogues, summarisation. For example, the article on Scribbr says that our ChatGPT model uses "reinforcement learning from human feedback (RLHF)" to reward helpfulness and discourage incorrect or harmful answers.

• Instruction-tuning may involve giving the model prompt-response pairs to follow instructions (e.g., "Write a short summary of …"), so that it learns how to behave when presented with a user prompt.

Together, this training pipeline allows the model to go beyond simple next-word prediction and to follow user instructions, engage in conversation and generate relevant outputs.

4. Inference: From user prompt to response

When you as a user type a question into ChatGPT or a similar platform, here's what happens in simplified practical steps:

1. The prompt is tokenised (split into sub-words/tokens) and converted to embeddings.

2. The model uses its internal layers (transformer blocks) to process the embeddings, incorporating context (including prior dialogue history).

3. It computes probability distributions over the vocabulary for what the next token should be. It selects one (or more) tokens according to a decoding strategy (greedy, beam search, top-k, top-p).

4. That token is appended, the model repeats for the next token until stopping criteria (max length or end-of‐response token) is reached.

5. The tokens are converted back to human-readable text and returned.

6. Behind the scenes, additional layers of moderation or safety filtering may intervene (e.g., to prevent disallowed content).

From a practical viewpoint: when we use ChatGPT, we are interacting with a model that is doing rapid statistical predictions at scale (often thousands or millions of operations) to choose each next word, guided by its training and the context we provided.

5. Infrastructure, scale and statistics

Operating a platform like ChatGPT or Google's Gemini involves huge infrastructure, large model sizes and considerable computational cost. Some relevant statistics:

• These models may contain hundreds of billions of parameters (weights).

• They are trained on "mitts of" (many) trillions of tokens (words/sub-words) from across the web.

• According to one data-source, almost 67% of organisations use generative-AI products (which rely on LLMs) in 2025.

From our hands-on experience working with AI systems, the practical implications are:

• Latency: generating responses in interactive settings requires low-latency inference, meaning high-performance hardware (GPUs/TPUs) plus optimised serving infrastructure.

• Memory & context window: The model must manage "context window" (how many past tokens it can attend to). Larger windows allow more context but require more compute.

• Fine-tuning & safety: Because these models may "hallucinate" (give plausible-looking but false answers) or produce undesirable outputs, additional filters, human-in-loop training and reinforcement learning feedback are applied. For example, one study found that ChatGPT only had ~63.4% accuracy in 10 different reasoning categories and still faced hallucination problems.

6. Practical perspective: how we experience using it

From a user perspective, when we type "Explain how solar panels work" or "Write a business email for me", here's what's going on:

• The system picks up our prompt, feeds it into the model.

• The model draws on its "learned world" from training (grammar, common knowledge, domain knowledge) and the immediate context (our prompt plus conversation history) to generate an answer.

• The answer is not simply retrieved from a database of fixed responses — rather it is generated token by token based on probabilities and context.

• If we ask follow-up questions, the context window captures prior dialogue so the model can maintain coherence (e.g., remembering "you said earlier that …").

• As the user, we benefit from the model's general knowledge, conversational ability and adaptability. But we must also remain aware that it may not always be correct or reliable (especially for factually critical or domain-specific tasks).

7. Why it “feels” human-like

There are several reasons why platforms like ChatGPT feel surprisingly human:

• Large models have seen huge numbers of real-world examples of language and can replicate style, tone, structure.

• The transformer’s attention mechanism allows long-range dependencies, so the answer can build over several sentences coherently.

• Instruction-tuning and RLHF (reinforcement learning from human feedback) align the model’s responses to human preferences (helpfulness, clarity, tone). For example, the Scribbr article notes that ChatGPT uses RLHF to reward helpful answers and discourage inappropriate ones.

• Because we humans are used to conversations with memory, the fact that the model can refer back to earlier parts of the dialogue (within its context window) helps it seem more personal and “aware”.

8. Key limitations to keep in mind

From our professional vantage, it’s imperative to recognise that while these platforms are powerful, they are not perfect. Key limitations include:

• Hallucinations: The model may generate plausible-looking answers that are incorrect or fabricated. As one academic paper found: in ~50% of cases the references produced by a model like ChatGPT did not exist or did not support the answer.

• Knowledge cutoff / stale data: Many models are trained up to a certain date and do not know events or data beyond that.

• Context window limits: If the conversation grows too long, earlier parts may be “forgotten” or truncated.

• Bias or undesirable outputs: Because training data reflect human texts, biases may be inherited; safety filters and RLHF attempt to mitigate but cannot eliminate them entirely.

• Compute cost & accessibility: Running very large models requires substantial hardware and energy; smaller or leaner models may sacrifice performance.

• No real “understanding”: Although responses appear coherent, the model does not truly “understand” language or the world in the human sense; it is still statistical prediction. As one commentary emphasises: “The only thing an AI model is doing … is predicting the next word.”

9. Summary: our takeaway

To summarise our experience: when we use a platform like ChatGPT, we are interacting with a powerful system built on deep-learning models (transformers) trained on massive data, fine-tuned with human feedback, served at scale, and deployed in real-time to answer our prompts. The key pieces are: large model size + transformer architecture + vast training data + inference infrastructure.

From a practical perspective, this means that the chatbot can serve a very wide range of use-cases (from casual conversation to writing code to summarisation). But it also means we need to use it wisely — checking critical facts, being aware of limitations, and understanding that it is not a substitute for domain experts.

FAQs

1. What does “large language model” (LLM) mean?
A large language model is a deep-learning system trained on vast amounts of text data that learns to predict what word (or token) comes next in a given context. Over time, such models pick up grammar, structure, some factual knowledge and can generate human-like text.

2. How does ChatGPT generate responses to my prompts?
When you input a prompt, the system tokenises it, processes it through its neural-network layers (transformer blocks with self-attention), predicts the next word, then the next, and so on until a response is formed. This process happens rapidly and uses the model’s learned parameters.

3. Why can these chatbots answer so many different types of questions?
Because the underlying model was pre-trained on a very broad data set and fine-tuned for dialogue and instructions, it has broad “knowledge” (up to its training cut-off) and the ability to generate text in many domains. That gives them versatility — from summaries, writing, code to translation and conversation.

4. Are there limitations or risks I should be aware of?
Yes — the models can hallucinate (generate wrong/inaccurate answers), may not have the most up-to-date information, have finite context windows (so past conversation may be forgotten), and may reflect biases from training data. You should always validate critical facts and use caution.

5. What role does human feedback play in how these chatbots behave?
Human feedback is critical: after pre-training, many models undergo fine-tuning where human evaluators rate outputs, creating reward models that guide the system via reinforcement learning (RLHF). This helps align the model’s outputs with desired behaviours (helpful, safe, relevant).