A context engineer is building a Databricks agent for a financial research platform that answers analyst queries using a vector search index over earnings reports. The agent uses a 180-token system prompt, a 250-token conversation history window, and a RAG pipeline that retrieves 8 document chunks at runtime. Each chunk is 550 tokens, contributing 4,400 tokens of retrieved context. The model’s context window is 8,192 tokens, leaving only 3,362 tokens for the analyst query and model response. Profiling reveals that retrieved chunks consistently contain repetitive regulatory boilerplate and duplicated table headers that are rarely cited in the model’s final answers. Which two changes should the context engineer make to reduce the token footprint of retrieved context without discarding relevant financial content? (Choose two.)
Select 2 answers.
Marked answer: A, C
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Chose A0
,C
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