Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
A field report from rolling out retrieval-augmented generation in production, including cache bugs, bad embeddings, and how we fixed them.
Compare popular embedding models including OpenAI, Sentence-BERT, and open-source alternatives. Learn which model fits your RAG, search, or similarity tasks.
Learn proven strategies to reduce AI inference costs including model quantization, caching, batching, and efficient prompt design. Real-world cost savings examples.
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.
Learn how to monitor AI models in production. Track performance, detect drift, and ensure model reliability with comprehensive observability strategies.
Learn how to build multi-agent AI systems where multiple AI agents collaborate to solve complex tasks. Architecture patterns and implementation guide.
Master prompt engineering techniques to get better results from LLMs. Learn about few-shot learning, chain-of-thought, and advanced prompting 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.
