GPT
AI/dictionary/gpt
Definition
OpenAIs family of large language models — Generative Pre-trained Transformer. GPT-4 (2023) and successors are the most widely used closed-source LLMs in production.
Posts that use this term
- Local agents and tool use
Function calling on open models in 2026: which models actually work (Qwen 2.5, Hermes 3, Llama 14B+), why local agents fail when they fail, and how to build defensive scaffolding around them.
- Picking a local model by task
The 2026 open leaders by task: coding (Qwen 2.5 Coder, DeepSeek-Coder), chat (Llama, Qwen, Mistral), small-model renaissance (Phi-3, Gemma 2), structured output, multimodal, embeddings.
- The pitch for local LLMs in 2026
Why every engineer should run a local LLM in 2026: privacy, zero marginal cost, lower latency, no rate limits, and offline. Even a 16GB MacBook Air runs Llama 3.2 3B at 30 tok/s.
- What leaves your machine when you use AI
What providers actually see, log, and retain when you call an LLM API in 2026. What 'we don't train on your data' really means, free vs paid tier differences, and when local is the only safe option.
- LLM APIs and the economics of tokens
How input vs output tokens are priced, why output is 5-6x more, what prompt caching saves you (10x), and the hidden costs (tokenizer drift, reasoning tokens, tool-call loops) that surprise people.
- The major LLMs in 2026
A tour of the closed frontier models (Claude, GPT, Gemini) and the open weights (Llama, Qwen, DeepSeek, Mistral). What 'B' means, what each is good at, and which size to actually run.
- The context window, and why models hallucinate
An LLM only sees a fixed-size slice of text at a time. When it doesn't know something, it predicts anyway — that's a hallucination, not a bug.
- From models to LLMs
An LLM is one kind of ML model — trained on text, predicts the next token. That single trick at scale gets you ChatGPT, and also explains where it breaks.
- How a model learns: training and inference
Training is the expensive one-time event where a model's numbers get tuned. Inference is the cheap repeated use afterwards. The gap in cost is enormous, and it shapes the whole industry.
- What makes a model: data and algorithm
A model is a file of learned numbers, produced by running an algorithm over data. Both ingredients matter, but bad data beats a good algorithm every time.
- Inside AI: machine learning and deep learning
Open the AI umbrella. Machine learning is the part that learns from data. Deep learning is ML done with neural networks — and that's where today's models live.