Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Master prompt engineering techniques to get better results from LLMs. Learn about few-shot learning, chain-of-thought, and advanced prompting strategies.
A field report from rolling out retrieval-augmented generation in production, including cache bugs, bad embeddings, and how we fixed them.
Complete guide to deploying AI models in production. Learn about model serving, containerization, scaling, and monitoring strategies.
Learn how to reduce LLM model size and inference costs using quantization techniques like Q4, Q8, and GPTQ. Practical guide with benchmarks.
Compare the top vector databases for AI applications. Learn when to use Pinecone, Weaviate, or ChromaDB based on your requirements.
Learn how to build production-ready RAG applications using vector databases, embedding models, and LLMs. Complete guide with code examples and best practices.
Run retrieval-augmented generation at scale. Chunking, caching, and observability.
AI Inference Cost Optimization. Practical guidance for reliable, scalable platform operations.
