AI & Machine Learning

LLM API costs are the cloud computing bill of the AI era. They start small during development, grow linearly during pilot programs, and explode exponentially when you ship to production traffic. We have seen teams go from $500/month during prototyping to $50,000/month within weeks of

LangChain has become the default framework for building LLM applications. It has 65,000+ GitHub stars, extensive documentation, and integration with every LLM provider and vector database on the market. It is also one of the most controversial tools in the AI engineering community, with vocal

LLM responses are slow. GPT-4 generates tokens at roughly 20-40 tokens per second. For a 500-token response, that means a 12-25 second wait before the user sees anything. Without streaming, your AI-powered feature feels broken. Users stare at a spinner, wonder if the application crashed,

Getting an LLM to return valid JSON sounds trivial. Ask for JSON, get JSON. In practice, LLMs produce invalid JSON at a rate between 2% and 15% depending on the model, prompt complexity, and output schema. For a feature that makes 10,000 API calls per

Choosing an LLM provider is one of the most consequential technical decisions you will make in 2023. It affects your cost structure, latency profile, compliance posture, and feature velocity for years to come. It is also a decision that most teams make badly, either by

The AI agent hype cycle is in full swing. Every week a new framework promises autonomous agents that can browse the web, write code, manage your calendar, and negotiate your rent. The demos are impressive. The production reality is different. Most agent systems are fragile,

Most prompt engineering advice comes from people tinkering with ChatGPT. They share tips like “be specific” and “give examples” as though these insights are revelatory. Building prompts for production applications is a fundamentally different discipline. You are not crafting a single clever instruction; you are

Enterprise search is broken in most organizations, and almost everyone has accepted the brokenness as normal. The search bar exists on the company intranet, the document management system, the knowledge base, the wiki, the ticketing system, and a dozen other tools. Users type a query,

Traditional monitoring assumes deterministic systems. You send the same input, you get the same output, and any deviation is a bug worth investigating. AI-powered applications break this assumption completely. A language model might return different text for the same prompt on consecutive calls. A recommendation