Context distillation is the process of selecting, filtering, and compressing input data so only the most relevant information is passed to an LLM's context window. It reduces token usage, lowers costs, improves latency, and produces more consistent outputs in enterprise AI workflows. The post covers how it works step-by-step, common techniques (prompt-based supervision, synthetic dataset generation, on-policy distillation, iterative refinement), real-world examples, and how it differs from model distillation and fine-tuning. Meilisearch is presented as a retrieval layer that supports context distillation pipelines by converting documents to vector embeddings and feeding relevant chunks to LLMs.
Table of contents
What is context distillation in AI?Why is context distillation important for LLMs?How does context distillation work?What problems does context distillation solve?What are the limitations of context distillation?What are common context distillation techniques?What are real examples of context distillation?When should you use context distillation?How is context distillation different from model distillation?Is context distillation better than LLM fine-tuning?How can Meilisearch support LLM context workflows?Why context distillation matters for modern AI systems383 Impressions