Jacopo Aliprandi’s Post

We’ve seen how AI assistants have transformed software engineering. But we can't just copy-paste that success into the data world. The reason is simple: software engineering and data engineering require fundamentally different contexts. An AI for writing code needs to understand the local context of a repository: syntax, libraries, and logic. An AI for analyzing data needs to understand the global context of the entire business: What does "active customer" mean? What are the governance rules for this data? What is its lineage? This is a semantic challenge, not a syntactic one. To make AI a reliable partner for data, we need to shift our focus from just the AI model to architecting the context it consumes. I put my thoughts together on what this means and why it can be the key to unlocking trustworthy AI in the enterprise. Read the full article here: https://lnkd.in/dFP4sATb Would love to hear your perspective on this. #AIContextEngineering #DataGovernance #GenerativeAI #DataEngineering #DataStrategy