Blog
Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Operational Checklist: AI Inference Cost Optimization
AI Inference Cost Optimization. Practical guidance for reliable, scalable platform operations.
Operational Checklist: RAG Retrieval Quality Evaluation
RAG Retrieval Quality Evaluation. Practical guidance for reliable, scalable platform operations.
Operational Checklist: Prompt Versioning and Regression Testing
Prompt Versioning and Regression Testing. Practical guidance for reliable, scalable platform operations.
Operational Checklist: LLM Gateway Design for Multi-Provider Inference
LLM Gateway Design for Multi-Provider Inference. Practical guidance for reliable, scalable platform operations.
Architecture Review: AI Inference Cost Optimization
AI Inference Cost Optimization. Practical guidance for reliable, scalable platform operations.
Architecture Review: RAG Retrieval Quality Evaluation
RAG Retrieval Quality Evaluation. Practical guidance for reliable, scalable platform operations.
Architecture Review: Prompt Versioning and Regression Testing
Prompt Versioning and Regression Testing. Practical guidance for reliable, scalable platform operations.
Architecture Review: LLM Gateway Design for Multi-Provider Inference
LLM Gateway Design for Multi-Provider Inference. Practical guidance for reliable, scalable platform operations.
AI Security and Safety: Protecting Your AI Applications
Learn how to secure AI applications against prompt injection, data leakage, and adversarial attacks. Best practices for AI security in production.
Embedding Models Comparison: Choosing the Right Model for Your Use Case
Compare popular embedding models including OpenAI, Sentence-BERT, and open-source alternatives. Learn which model fits your RAG, search, or similarity tasks.
AI Cost Optimization: Reducing LLM Inference Costs by 80%
Learn proven strategies to reduce AI inference costs including model quantization, caching, batching, and efficient prompt design. Real-world cost savings examples.
Fine-tuning vs Few-Shot Learning: When to Use Each Approach
Compare fine-tuning and few-shot learning for adapting LLMs. Learn when to use each approach and their trade-offs in terms of cost, performance, and complexity.