Browsing: Llm Applications

I recently covered TypesafeAI’s JEV in TDS (link at the end). JEV makes fast decisions, such as classifying content or choosing the next step in a workflow, and returns typed answers that software can use directly. The accompanying probabilities help an application decide whether to act on an answer or pass it to a person…

There’s a category error that shows up in many LLM systems: We retrieve a few documents, put them in context, generate a fluent answer with links, and then we call the answer “grounded.”

I built the whole thing in pure Python and ran the tests myself, with the results shown as pass or fail.

Retrieval-Augmented Generation (RAG) is the most widely used LLM use case across organizations. By vectorizing documents and retrieving semantically similar chunks at query time, RAG mitigates hallucinations, grounds responses and bypasses static knowledge cutoffs imposed by model pretraining. However, in a real-life scenario, standard vector-based RAG runs into limitations for complex queries, such as those…