Teknosys learning library

The decoder branch: GPT-style language models

The decoder branch models a sequence as a series of next-token predictions. Its causal attention means a position cannot use later target tokens to solve its prediction task. GPT-2 is a clear historical example; many contemporary text generators preserve this basic pattern while changing positional methods, normalization, attention, and feed-forward layers.

One sequence contains both prompt and continuation

A decoder-only application typically tokenizes the prompt and then extends it. For support-reply drafting, the prompt can contain the ticket, permitted policies, and instructions. The model predicts a token, adds it to the prefix, and repeats. It does not normally run a separate bidirectional source encoder connected through the original transformer's cross-attention sublayer.

Chat interfaces often serialize system, user, and assistant messages with a checkpoint-specific template. A base pretrained model and an instruction-tuned model may accept the same architecture but behave very differently. The template, special tokens, and instruction tuning matter; a generic string labeled “system prompt” is not a universal protocol.

Probability is not factual verification

Training rewards prediction of observed text under the selected objective. It does not inherently verify a shipment database or check whether a promised refund is authorized. A fluent reply can invent details. Give the application trusted information through appropriate retrieval or tools, and validate consequential outputs before acting on them.

Ticket evidence: package arrived; charger missing
Allowed response: ask for order details and explain the next support step
Unsupported response: claim that a refund has already been issued

Evaluation should distinguish those outcomes even if both replies are fluent.

Generation settings and systems costs

Temperature rescales logits before sampling; low temperature typically concentrates probability more strongly. Top-k restricts sampling candidates by count, while top-p uses a cumulative probability threshold. These settings change variety and determinism, not the model's access to real facts. For a structured workflow, test output validity and failure recovery rather than selecting a setting based only on a few impressive answers.

Long prompts take work to process, and a growing key-value cache consumes memory during generation. Cache sharing, batching, quantization, and attention variants change deployment trade-offs. Compare time to first token separately from subsequent-token throughput, and include input/output lengths in benchmark reports. Tokens per second without that context can be misleading.

Use the branch beyond chat

A causal model can complete code, rewrite a paragraph, generate a label, or score candidate continuations. For fixed-label classification, ensure generated wording maps unambiguously to labels and compare against a dedicated classifier. For code, run tests in an appropriate isolated environment and do not assume syntactic plausibility establishes correctness.

Finally, do not treat prompt instructions as a complete security boundary. User-controlled text and retrieved documents can contain conflicting instructions. Restrict tool permissions, keep secrets out of prompts, and use explicit validation around any action. Architecture explains how a continuation is produced; it does not by itself provide safe application behavior.

Sources: GPT-2 documentation; decoder-family overview.

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