Context Engineering: Can you trust long context?

Modern large language models (LLMs) have significantly expanded their context windows, supporting up to 1 million tokens, which enhances their ability to process more comprehensive information in applications like Retrieval-Augmented Generation (RAG) and agentic workflows. This development has led to the emergence of "context engineering," an approach that focuses on optimizing the inclusion of relevant context in LLM prompts to ensure efficient and accurate outputs. Despite the potential of longer contexts, LLMs can suffer from degraded performance, particularly struggling with the "Lost in the Middle" effect, where information located in the middle of a long input is often less accurately processed. To mitigate these challenges, techniques like context distillation, which involves filtering and strategically organizing essential information, are employed to improve LLM performance and reduce the likelihood of generated hallucinations. This careful orchestration of context is essential for building trustworthy AI systems that produce more accurate and transparent outputs.