What is context distillation in AI & how does it improve LLM efficiency?

This post introduces and explains context distillation as a technique to improve LLM efficiency by identifying, filtering, and compressing input data before it's passed to the model. It details why context distillation is important (cost, efficiency, performance), how it works through a step-by-step process, and the problems it solves (limited context windows, prompt complexity, inconsistent outputs). Limitations such as setup time, potential for over-optimization, and loss of flexibility are discussed. Common techniques like prompt-based supervision, synthetic dataset generation, on-policy distillation, and iterative refinement are presented with real-world examples. The post clarifies the differences between context distillation, model distillation, and LLM fine-tuning. It highlights how Meilisearch can support LLM context workflows by acting as a precision layer for retrieving relevant chunks from a knowledge base, converting documents to vector embeddings, and reducing hallucinations.