Building Context-Aware Search in Python with LLM Embeddings + Metadata
Keyword search breaks the moment a user types something a document doesn’t literally say.
Keyword search breaks the moment a user types something a document doesn’t literally say.
I have been experimenting with the OpenAI Agents SDK, and it has quickly become one of my favorite ways to build agentic AI applications.
Here is the number that defines the current state of things:
You have probably spent time learning how to prompt AI well.
Search works well when users know exactly what they are looking for, but it breaks down when intent is described in natural language.
Most
Large language models (LLMs) now power everything from customer service bots to autonomous coding agents.
Agentic loops in production can be synonymous with high costs, especially when it comes to both LLM and external application usage via APIs, where billing is often closely related to token usage.
AI agents have evolved beyond passive chatbots.
Overview of adaptive parallel reasoning. What if a reasoning model could decide for itself when to decompose and parallelize independent subtasks, how many concurrent threads to spawn, and how to coordinate them based on the problem at hand? We provide a detailed analysis of recent progress in the field of parallel reasoning, especially Adaptive Parallel …
Read more “Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling”