AI & ML Research 3 Days

Last updated: July 7, 2026, 8:31 AM ET

AI Architecture & Scaling

Organizations are broadening their AI applications as agentic systems gain traction, necessitating a focus on foundational architectural elements for effective scaling scale AI architecture. As AI capabilities rapidly advance, IT leaders must adapt their strategies to accommodate this continuous evolution and its associated risks. This shift toward more sophisticated AI deployments requires robust frameworks that can support expanding use cases and growing data demands.

LLM Development & Testing

The pursuit of more reliable and efficient large language models (LLMs) is driving innovation in development and testing methodologies. Researchers are exploring methods to set up your own LLM, indicating a growing interest in self-hosted solutions. Concurrently, strategies for validating model outputs are becoming paramount. Techniques such as validating RAG answers before user exposure, employing structured output for evidence checks, and accepting "not-found" scenarios are crucial for preventing hallucinations. A "typed answer contract" that defines a schema for each LLM query aims to prevent inaccurate responses by making every answer checkable stop returning text RAG. Furthermore, end-to-end testing is being emphasized to increase the effectiveness of coding agents run end-to-end tests.

RAG Prompt Engineering

Effective Retrieval Augmented Generation (RAG) systems depend heavily on refined prompt engineering. A methodology has been proposed to assemble RAG generation prompts by combining a base prompt with specific rules tailored to each question, managed by a dispatcher that translates parsed questions into typed LLM