10 Rules for Getting the Most Out of GLM 4.7 on Cerebras
GLM 4.7 is the strongest open-source coding model available—but are you prompting it correctly? We've seen overwhelming demand from developers migrating to GLM 4.7, and the biggest mistake? Reusing old prompts without adjusting for how this model actually behaves. Sarah Chieng, Head of DevX at Cerebras, presents 10 rules to help you get frontier-level performance at 1,500 tokens per second. The Rules: 1. Front-load your instructions 2. Be clear and direct (use "must" and "strictly") 3. Specify a default language 4. Leverage roleplay and personas 5. Break complex tasks into smaller steps 6. Disable reasoning when you don't need it 7. Enable reasoning for complex tasks 8. Use critic agents for validation 9. Pair GLM with a frontier model for hybrid workflows 10. Use clear thinking intentionally Why GLM 4.7? → 96% on Tau² Bench Telecom (tool use reliability) → 86% on GPQA Diamond (scientific reasoning) → 131K context window, 40K output tokens → 15x faster than closed-source competitors on GPUs → Fraction of the cost of models like Claude Sonnet 4.5 The bottom line: By tuning your prompts, architecture, and sampling parameters to match GLM 4.7's behavior, you can build faster, cheaper, and more scalable systems—without sacrificing intelligence. 🚀 Get started for free: cloud.cerebras.ai +++ Subscribe to our channel! https://www.youtube.com/@Cerebras Cerebras builds the world’s largest AI chip — delivering up to 20× faster inference than leading GPUs. Our mission is to engineer the future of compute and make state-of-the-art AI accessible to every team. Explore our newest open-source models and get free compute at http://cerebras.ai/ . Watch our full video library: https://youtube.com/@Cerebras/videos Read the latest engineering deep dives on our blog: https://cerebras.ai/blog Explore our systems and technology: https://cerebras.ai/publications Follow Cerebras on X: https://x.com/cerebras Connect with us on LinkedIn: https://linkedin.com/company/cerebras-systems