Weekly AI Knowledge Sharing

One of the most underrated topics in AI right now: MCP (Model Context Protocol) Everyone talks about: better models smarter agents prompt engineering But the real enterprise challenge is: How do AI systems reliably connect with tools, databases, and applications? LLMs alone don’t know:…

Everyone is excited about AI agents. But here’s the uncomfortable truth: 👉 Agents are only as good as the data they use. You can design the best workflows, prompts, and tools —but if your data is weak, your system will fail. 🔹 What really…

One of the biggest mistakes in GenAI is building a chatbot first and searching for a problem later. A real RAG project should always begin with a real use case. For example: customer support assistant, internal knowledge bot, policy search, medical document Q&A, legal…

The biggest limitation of an LLM is simple , it doesn’t know your data. That’s where RAG (Retrieval Augmented Generation) becomes powerful. Instead of retraining the model, we: 🔹 Store enterprise data in Delta Lake 🔹 Convert it into embeddings 🔹 Use Vector Search…

So here’s a high-level breakdown, without the noise. Step 1: Start with the problem, not the modelBefore touching any AI tool, be clear on why you need a chatbot.Is it for customer support? Internal knowledge? Automation?Most chatbot failures start with an unclear use case….

“Is AI Product Manager a real long-term career, or just hype?” It’s real — and it’s growing fast. Today, almost every serious product roadmap includes AI. But AI alone doesn’t create value. Products fail when AI is built without clear user problems, success metrics,…