Transformers and their attention heads encode a fixed set of next-word probabilities from the training data. That part is deterministic.

🎲 The response to the prompt is generated during the decoding step, where variance appears. The system samples from that probability spread learned during encoding, using a mix of temperature, top-k, top-p, or beam search approaches.

🧬 This sampling was inherited from early language translation models to try alternate phrasings when no exact translation exists, and reused for possible answer generation when no good answer is found: often credited as model creativity, but truly a band-aid for missing data.

🎯 Similarity search is different. Given the same embedding model, index, and query, similarity scores and the ranking are stable and auditable. You still harness deep learning through embeddings, but you avoid the randomness introduced by generative decoding.

✅ Why this matters for DiscoLike: our product leans on embeddings-based company search not open-ended generation. That gives teams reproducible, explainable results for targeting and segmentation, with the quality of deep learning and the stability of classic search.

👉 Takeaway: if your use case needs consistent decisions and easy QA, use embeddings + similarity search. Save stochastic generation for tasks where variation is a feature, not a bug.

📚 Sources: deterministic logits vs. stochastic decoding with temperature, top-k, top-p, decoding methods overview, similarity ranking for dense retrieval, DiscoLike’s embeddings-based search.