GPT is a decoder-only transformer. BERT sees both left and right context. GPT only sees previous tokens. A GPT architecture workshop differs from an encoder-only workshop. It needs to cover left-to-only attention, token-by-token production, prompt engineering, and generation speed techniques.
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Why "GPT Uses Attention" Ignores the Critical Difference
Token i can only attend to tokens 0 through i. Autoregressive generation is sequential by design.
A representative from once told me: “A vendor claimed a GPT workshop. They showed attention visualizations. All tokens attended to all other tokens. 'That is BERT,' I said. 'GPT requires a causal mask.' They had not implemented masking. Their 'GPT' was actually an encoder. The audience was learning the wrong architecture. Now we verify causal masking in every GPT event.”
Pose these event organizer kl questions to coordinators: Do you visualize the difference between bidirectional (BERT) and causal (GPT) attention.
Why "The Model Generates Text" Is Vague
Training parallelizes across positions. Inference feeds its own predictions.
An NLP engineer in Selangor posted: “I attended a GPT workshop where the presenter showed fast generation. I asked 'are you using KV caching?' They did not know what that was. 'Then how are you generating so quickly?' 'We process the full sequence from scratch each time,' they said. That is O(n²) per token, not O(n). Their demo was inefficient and not production-ready. Now I ask for KV caching.”
Talk through with your coordinator: Do you cover optimization techniques like KV caching for faster inference.
The Difference between "Raw Generation" and "Controlled Generation"
GPT continues text based on input. Example-based prompting shows the desired format. Fine-tuned models follow system prompts.
Inquire with planners: Do you show how prompt design affects output quality.
Temperature and Sampling: Controlling Randomness
Greedy often produces repetitive, dull text. Stochastic generation is random. Temperature controls randomness.
Kollysphere agency advises showing how sampling parameters (temperature, top-k, top-p) affect output diversity and quality.
