Modern AI systems can appear confident while being surprisingly fragile. Change the order of the same evidence, rearrange the prompt, or present supporting facts in a slightly different sequence, and the model may produce a different answer. That is not always a retrieval problem. It is often a context presentation problem. This session explores why the layout of information matters, why repeated exposure to the same input can improve results, and what that tells us about how models process evidence before generating a response.

Using OpenSearch as the foundation, this session examines how RAG agent responses change when the same knowledge is retrieved, ranked, and presented in different ways. We will explore practical patterns for structuring search results, document chunks, tool outputs, and evidence chains to make models less sensitive to arbitrary ordering. Attendees will see how changes in context presentation can alter an agent's answer, then learn techniques for testing input-order sensitivity, reducing permutation-induced hallucinations, and building production AI systems that reason from stable evidence instead of whatever sequence the pipeline happened to cough up.