Trusted AI Adoption Using Fine-Tuned Reinforcement Learning

#cnsv #AI #reinforcement-learning

This is an online event. Pre-registration is required.

Trusted adoption of an AI solution in an enterprise setting has many requirements. Large Language Models (LLMs) can often be constrained by the risks of hallucination bias and limited operational trust and reliability. Overcoming these challenges requires models whose output is fully grounded, auditable, and accurate. In addition, enterprise policy compliancy is critical in highly regulated sectors such as finance, healthcare, legal, insurance, government, and critical infrastructure.

This presentation will discuss a solution framework with open-source models that can be systematically adapted using a combination of post-training optimization with advanced and agentic Retrieval-Augmented Generation (RAG) to create a production-ready enterprise LLM architecture. Advanced RAG is able to strengthen factual grounding through hybrid retrieval, metadata filtering, reranking, citation generation, and context optimization. Agentic RAG is able to extend this capability by enabling planner, retriever, verifier, and critic agents to decompose complex tasks, iteratively retrieve evidence, validate sufficiency, re-query when needed, and produce auditable answers suitable for enterprise production use.