Prompt Engineering Best Practices: Maximizing LLM Performance
KU
Kiril Urbonas
5 months ago • 4 min read•Updated Feb 21, 2026•0 views
Master prompt engineering techniques to get better results from LLMs. Learn about few-shot learning, chain-of-thought, and advanced prompting strategies.
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Solve this problem step by step:
Problem: A Kubernetes cluster has 10 nodes. Each node can run 20 pods. If we need to deploy 150 pods, how many additional nodes do we need?
Solution:
Step 1: Calculate total capacity: 10 nodes × 20 pods = 200 pods
Step 2: Current requirement: 150 pods
Step 3: Additional needed: 150 - 200 = -50 (we have enough capacity)
Step 4: Answer: 0 additional nodes needed
You are a senior cloud architect with 15 years of experience. Design a multi-region AWS architecture for a high-traffic e-commerce platform. Consider:
- High availability
- Disaster recovery
- Cost optimization
- Security
For Prompt Engineering Best Practices: Maximizing LLM Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Prompt Engineering Best Practices: Maximizing LLM Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Prompt Engineering Best Practices: Maximizing LLM Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
