Introduction You have a large language model. It is powerful, but it does not know your specific domain. It was trained on public internet text—not your internal documents, not your product catalog, not your customer support history. You need it to understand your world. How do you make that happen? Two approaches dominate the conversation: fine-tuning and retrieval-augmented…
1) Start with the Reality: Why Observability Matters Building AI agents is exciting—but running them in production is where most challenges appear. Common problems include: This is why observability is essential. Observability allows you to understand exactly what your AI agent is doing at every step—from input to final output. Platforms like LangSmith, developed by…
Introduction Computers do not understand words. They understand numbers. This simple fact is the foundation of all modern AI. Before a machine can process language, it must convert text into a form it can work with. That conversion happens through embeddings. Embeddings are the unsung heroes of modern AI. They power semantic search, recommendation engines, retrieval-augmented…