Treating prompts as versioned, tested, observable production code — prompt structure, few-shot examples, chain-of-thought, template reuse, compression for cost, and catching prompt regressions before users do.
Scaling RAG past a demo: choosing a vector database, chunking strategy, hybrid search and reranking, measuring retrieval quality, and where the cost actually goes at high query volume.
How to build an LLM agent that actually finishes multi-step tasks — tool-calling mechanics, the ReAct planning loop, state management across steps, and the guardrails that keep it from hallucinating its way into a bad action.
FastAPI has become the go-to Python framework for serving ML models in production. Here's how to build async inference endpoints, stream LLM responses, and deploy them reliably on AWS.
A complete guide to building retrieval-augmented generation (RAG) systems in Java using LangChain4j. Learn chunking strategies, embedding pipelines, vector store integration, and how to ship RAG to production.
Step-by-step guide to packaging a scikit-learn or PyTorch model as a Lambda function — covering cold starts, container images, model versioning, and A/B testing on AWS.