Beyond the Final Prompt: How Conversation Context Changes AI Answers

A study submitted to arXiv on 3 Aug 2026 found that omitting within-conversation context changes AI answers materially in 44.7% of cases (95% CI 33.8%–56.1%), based on 180 English multi-turn conversations from a governed commercial corpus and the PRISM dataset. Full-conversation answers scored 0.49 points higher on a 0–4 request-satisfaction scale, and adding a compressed prefix reduced the material-difference rate to 30.8%, though compression was not equivalent to full context.

Computer Science Information Retrieval Submitted on 3 Aug 2026 Title:Beyond the Final Prompt: Measuring the Effect of Within-Conversation Context on AI Answers View PDF /pdf/2608.02556 HTML experimental https://arxiv.org/html/2608.02556v1 Abstract:An isolated final user message is often treated as the query in evaluations of AI systems. In a conversation, however, the actionable request may be distributed across preceding turns. We directly test whether that omitted within-conversation context changes answers. For each of 180 English multi-turn conversations sampled from a governed commercial corpus and the public PRISM dataset, we hold the final user message and requested answer model constant while generating three answers: one from the full role-labelled conversation, one from the final message alone, and one from the final message plus a prefix-only reconstruction capped at 160 words. A separately requested judge model evaluates answers under randomized labels. The prespecified primary endpoint is a material difference that could change what the user does, rather than a difference in style or detail. After inverse-probability weighting to the eligible cohorts, the full-conversation and isolated-final answers differ materially in 44.7% of cases 95% bootstrap CI 33.8% to 56.1% . Full-conversation answers score 0.49 points higher on a 0 to 4 request-satisfaction scale 0.32 to 0.67 . Adding the compressed prefix reduces the material-difference rate to 30.8% 20.2% to 42.1% , a 13.9-point reduction 4.9% to 24.1% , and reduces the mean satisfaction gap to 0.01 points -0.12 to 0.13 . Yet compression is not equivalent to the complete dialogue context: almost one third of answers remain materially different. An order-swapped repeat on 48 cases yields 91.7% agreement and kappa = 0.83 for the primary decision. The study concerns preceding turns in the same conversation and does not test persistent memory across separate conversations. Additional Features References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .