You are troubleshooting a Claude integration where responses have gradually become less accurate and less relevant to user queries over the past…

The gradual decline in accuracy and relevance, specifically following the introduction of new products and company policies, strongly indicates that the model is operating on outdated context. Large Language Models like Claude rely on Retrieval-Augmented Generation (RAG) or injected system prompts to access current, proprietary business data. If the underlying knowledge base is not updated to reflect recent organizational changes, Claude will hallucinate or provide obsolete answers based on stale context. Refreshing the knowledge sources ensures the model retrieves and synthesizes the most current information.

A. Model knowledge cutoffs are static dates; they do not "expire" or degrade gradually over a single month.

C. API rate limits restrict the volume of requests (returning 429 errors) but do not degrade the qualitative accuracy of successful responses.

D. While user expectations can shift, this ignores the explicit operational change (new products/policies) causing the factual drift.

1. Anthropic Claude Documentation, "Retrieval Augmented Generation (RAG)", Section: Keeping Data Fresh.

2. Stanford University CS224N: Natural Language Processing with Deep Learning, Lecture 11: Prompting, Instruction Tuning, and RLHF (Context Injection).