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Foundation models excel at generic coding tasks but fail in enterprise environments because they lack institutional knowledge—internal APIs, architectural decisions, deprecated patterns, and company-specific constraints. The solution is a context layer built on verified internal knowledge, exemplified by Stack Overflow's Internal product and Uber's Genie assistant. Genie uses RAG to combine Stack Overflow Internal's human-validated Q&A knowledge base with OpenAI's language models, enabling Uber engineers to get accurate, attributable, company-specific answers at scale. Key challenges in building such a context layer include the cold start problem, knowledge maintenance burden, cultural resistance to documentation, and privacy/security controls—each with practical mitigation strategies.

The context problem: Why enterprise AI needs more than foundation models