Trustworthy AI: Optimizing Content for Large Language Models

Trustworthy AI is the strategic target for every organization striving to turn information into intelligence. Achieving this goal starts with well-structured, high-quality content that provides a reliable foundation for reasoning and response.

As large language models (LLMs) continue to evolve, their ability to handle vast amounts of information has expanded dramatically. Early LLM models were limited to 2,048 tokens (approximately 1,500 words). Today’s modern systems boast context windows of up to two million tokens. But…bigger isn’t always better. Research shows that as an LLM’s context window grows, accuracy can decline, sometimes significantly, due to what’s known as context rot.

The following conversation with DCL’s Systems Architect Rich Dominelli and David Turner unpacks how context management impacts LLM performance and explores tactics for preparing content that supports precise, reliable responses in AI-powered systems.