One common mistake is over-retrieval—injecting too many retrieved chunks into the prompt. Developers often assume that more context equals better answers, but excessive context increases noise and accelerates Context Rot. Including ten loosely relevant documents is usually worse than including three highly relevant ones.
Another frequent mistake is failing to separate context types. Mixing system instructions, retrieved documents, and conversation history without clear structure makes it hard for the model to understand what is authoritative. For example, placing retrieved text before system constraints can cause the model to follow the retrieved content instead of the instructions.
A third mistake is neglecting evaluation. Teams often implement retrieval once and assume it works. In reality, chunk size, retrieval limits, and ranking thresholds need tuning. Using a vector database like Milvus or Zilliz Cloud makes retrieval easy, but developers still need to monitor relevance and adjust parameters over time. Context engineering is an iterative process, not a one-time setup.