Blog
Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
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.
AI Observability and Monitoring: Tracking Model Performance in Production
Learn how to monitor AI models in production. Track performance, detect drift, and ensure model reliability with comprehensive observability strategies.
Multi-Agent AI Systems: Building Collaborative AI Applications
Learn how to build multi-agent AI systems where multiple AI agents collaborate to solve complex tasks. Architecture patterns and implementation guide.
Prompt Engineering Best Practices: Maximizing LLM Performance
Master prompt engineering techniques to get better results from LLMs. Learn about few-shot learning, chain-of-thought, and advanced prompting strategies.
AI Model Deployment Strategies: From Development to Production
Complete guide to deploying AI models in production. Learn about model serving, containerization, scaling, and monitoring strategies.
Model Quantization Techniques: Reducing LLM Size and Cost
Learn how to reduce LLM model size and inference costs using quantization techniques like Q4, Q8, and GPTQ. Practical guide with benchmarks.
Vector Databases for AI: Comparing Pinecone, Weaviate, and ChromaDB
Compare the top vector databases for AI applications. Learn when to use Pinecone, Weaviate, or ChromaDB based on your requirements.
Building RAG Applications: A Complete Guide to Retrieval Augmented Generation
Learn how to build production-ready RAG applications using vector databases, embedding models, and LLMs. Complete guide with code examples and best practices.