AI
aiThis section covers applied AI and machine learning for engineers — building, deploying, and operating systems powered by large language models and ML, with a strong focus on production concerns: cost, latency, reliability, and evaluation. Expect concrete numbers and field notes over hype.
Topics include LLM cost optimization, prompt and retrieval patterns, model selection and routing, and the MLOps practices that keep AI systems dependable as they scale.
Frequently asked questions
How do I reduce LLM (large language model) costs in production?
The biggest levers are caching prompts and responses, routing simple requests to smaller/cheaper models, compressing prompts and trimming context, batching asynchronous work, and fixing bugs like retry loops. Measuring per-request token cost first is what makes these gains visible.
What is MLOps?
MLOps applies DevOps practices to machine learning — versioning data and models, automating training and deployment, monitoring model performance and drift, and setting up evaluation gates — so ML systems stay reliable in production.
