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Prompt engineering only becomes interesting when you're prompting for structured, specific output that has to be reliable enough to feed downstream systems. A chatbot that gives a slightly different a
Most LLM features eventually need the model to return structured data like a JSON object your application can parse and act on. Connecting LLM output to a database, a UI component, or a downstream API
Automated evaluation using an LLM sounds like an elegant solution until you understand its failure modes. The model playing the role of a teacher grading work has four well-documented ways to get it w
Most LLM project ideas start from the technology. "What can I build with agents?" or "Let me try a RAG pipeline." That approach produces demos that are interesting for a day and abandoned by the weeke
RAG sounds complex until you break it into its actual steps: Query → embed query → search vector store → retrieve top N chunks → prompt + chunks → generate At its core, it's a retrieval problem with
One of the most important things to internalize about LLM tool use is what the model actually does (and doesn't do) when it "calls" a function. Anthropic never executes your code. The model reads the
When I started building on the Anthropic API, the first thing I had to stop treating as a detail was streaming. It's easy to prototype an LLM feature with a standard request-response cycle — send a me
