The Context Paradox: Why Your Fancy AI Fails in Production

1. 1Identify the limitations of foundation models in understanding internal systems
2. 2Build a knowledge layer with internal documentation and retrieval systems
3. 3Integrate the knowledge layer with AI models to provide context
4. 4Test and refine the models in real enterprise environments

AI engineers and data scientists can benefit from understanding the importance of context in AI models, as it can make their models more reliable and production-ready

💡 Building a knowledge layer with internal documentation and retrieval systems can make AI models more reliable and production-ready

🚨 AI models fail in production due to lack of context! 🚨