Right context, wrong assumption: what Morgan Stanley learned about prompting at scale
Morgan Stanley's deployment of an AI assistant for its financial advisors exposed a problem most teams overlook: feeding the model more information does not produce better answers. The real discipline is selecting which context matters, and why that distinction changes how you build prompts entirely.
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Retrieval-augmented generation (RAG): giving models your data
The context window: the model's working memory
Anatomy of a good prompt: role, context, task, constraints
Evaluating outputs: how do you know it works?
Working with long documents and big inputs
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