According to Avi Chawla on X, seven core text-generation parameters—temperature, top_p, top_k, repetition penalty, max_tokens, frequency penalty, and presence penalty—govern LLM output diversity, coherence, and safety, and are critical for production tuning (as reported by X post and linked article). According to the X post, lowering temperature and using constrained sampling like top_p improves determinism for enterprise workflows, while higher temperature and top_k broaden creativity for ideation. As reported by the X thread, repetition and frequency penalties reduce looping and token overuse, improving factual readability in customer support bots. According to the X article link, setting max_tokens controls latency and cost, enabling predictable spend for API deployments. For AI product teams, these levers create measurable business impact: higher determinism cuts human review time, and calibrated penalties reduce hallucination rates in RAG pipelines, according to Avi Chawla’s guidance on X.