A midsize company recently asked engineers to build an AI support agent that remembered everything. They fed it 50,000 words—catalogs, policies, and historical emails—assuming more data creates smarter responses. The result was a disaster: accuracy fell to 40-45%, and the bot lagged on simple queries.

Faraz Farhan, a senior prompt engineer at PowerInAI, discovered that overloaded context windows confuse large language models. When teams provide redundant policies or marketing fluff, LLMs struggle to separate signal from noise, often hallucinating answers by merging conflicting details. His team tested three approaches.

The full data dump failed. A cleaned 15,000-word version improved accuracy only slightly. However, stripping the context down to 1,500 words of operational facts—pricing, active policies, and specific exceptions—boosted performance to 80%.

The solution is a 'Memory Collapse Framework.' Instead of treating context like a library, treat it like RAM. Ditch narratives for atomic facts and move rarely used data to external APIs. By giving AI less to remember, you force it to focus on what actually matters.