Author: Sarah Chen

Load testing a traditional web application is well-understood: generate a realistic traffic pattern, measure response times and error rates at increasing load, find the breaking point, optimize. Load testing an AI application is a different beast entirely. The response times are orders of magnitude longer

Content summarization sounds simple: take a long document, produce a short version. In practice, building a summarization system that works reliably across document types, handles edge cases without hallucinating, respects length constraints consistently, and scales to thousands of documents per day is a genuine engineering

The Vector Database Decision If you’re building anything with embeddings — semantic search, RAG pipelines, recommendation engines, image similarity — you need somewhere to store and query vectors. The market has exploded with options, and the decision isn’t obvious. We’ve deployed all three of the

Every team building with LLMs eventually arrives at the same place: the model needs access to private data, and fine-tuning is either too expensive, too slow, or too rigid. Retrieval-Augmented Generation (RAG) is the answer most reach for. The problem is that most RAG implementations