ChatGPT

What it is

- A conversational interface built on large language models (LLMs). It generates text responses based on patterns learned from vast amounts of text.

- Useful for factual Q&A, drafting and editing text, brainstorming, tutoring, coding help, summarization, role-playing, and more.

How it works (high level)

- Trained on large datasets of text to predict the next word or token in context, which lets it generate coherent replies.

- Not a database of facts; it synthesizes outputs from patterns in training data rather than “looking up” facts at query time (unless connected to a live web or plugin).

Strengths / useful applications

- Fast drafting (emails, reports, essays, ads).

- Language tasks: translation, rewriting, summarizing.

- Coding help: explanations, debugging, examples.

- Tutoring and explanations adapted to different levels.

- Brainstorming and ideation.

- Conversational simulations and role play.

Important limitations

- Hallucinations: it can produce confident but incorrect or invented facts.

- Knowledge cutoff / recency gaps: models stop learning at a certain point unless they’re updated or given current data/plugins, so very recent events can be missing or wrong.

- No true understanding or beliefs — it patterns text but does not have consciousness or intent.

- Sensitive areas (medical, legal, financial, safety-critical): not a substitute for professional advice.

- Can reflect biases present in training data.

Safety, privacy, and data use

- Content policies restrict generation of harmful or disallowed content.

- Conversations may be logged and used to improve models unless you opt out — check the service’s privacy policy and settings for current details.

- Don’t paste sensitive personal, medical, financial, or private account information unless you trust the platform and understand how data will be used.

How to get better answers (prompting tips)

- Be specific: include context, desired length, audience, and format. Example: “Write a 150-word email to a client apologizing for a late invoice, with a professional tone.”

- Ask for sources or ask it to “explain how you know this” if you need verifiable claims.

- Request step-by-step reasoning for complex problems, or ask it to show its work.

- Ask it to adopt a role or perspective: “Explain quantum tunneling as if I’m a first-year physics student.”

- If an answer seems uncertain, ask follow-ups or request citations and verification.

- For code or technical answers, provide sample input/output and environment details.

When to verify externally

- Any factual claim that matters (dates, legal/medical recommendations, scientific findings, news).

- Code that will run in production or affect safety.

- Financial or legal decisions.

Extensions and integrations

- Many deployments offer plugin or browsing options to fetch live information, perform calculations, or interact with external services (availability depends on platform/settings).

- Some products offer multimodal inputs (text + images) or specialized models for code, images, etc.