Context engineering solves problems that appear when LLM applications move beyond short, single-turn interactions. The most common problems include inconsistent answers, ignored constraints, contradictions across turns, and declining accuracy in long conversations. These are not model failures; they are failures of unmanaged context.
One major issue it solves is Context Rot, where important information loses influence as prompts grow longer. Another is retrieval overload, where too many documents are injected into the prompt and the model cannot reliably focus on the most relevant ones. Context engineering also helps prevent hallucinations caused by missing or diluted grounding information, since the model is more likely to rely on retrieved evidence when the context is clean and focused.
From an operational perspective, context engineering also improves scalability and cost control. By retrieving only relevant chunks instead of dumping entire documents, token usage drops and latency becomes more predictable. Vector databases such as Milvus and Zilliz Cloud enable this by acting as external memory, allowing systems to scale knowledge without scaling prompt size. In short, context engineering turns LLMs from demos into reliable systems.