Gen AI

A supervisor and its specialists can route perfectly and still fail, if none of them remember what happened five minutes or five sessions ago.

The meeting had been going on for forty minutes and Siddharth was starting to feel uneasy.
His company was building an internal legal assistant. Lawyers would ask it questions about case law, contract clauses, and regulatory filings. The product team had done their research. The design was solid. The retrieval pipeline was working in staging. Everything looked good.

Open any System Design resource and you will see the same pattern: a question like ‘Design a URL Shortener’ quickly becomes a canvas filled with Kafka, Redis, API Gateways, Load Balancers, Kubernetes, CDNs, and half a dozen databases.

Vikram had read every article he could find about RAG.
He understood the concept. He could explain embeddings at a whiteboard. He knew what a vector store was, how chunking worked, and why retrieval quality depended so heavily on how you split your documents.

Arjun had been working on the same AI feature for three weeks.
It started simple enough. His team wanted a chatbot that could answer questions about their product documentation.

It’s 11:40 PM. Aditya is showing his manager the new AI assistant he built. “Ask it anything,” he says.
His manager types: “What were our total sales last week, and email the summary to the finance team.”

Last month she wired her company’s AI assistant into Slack. It took four days, custom auth, a webhook handler, a brittle little adapter to translate Slack’s API into something the model could call. It worked. Everyone clapped.

A Graph is a data structure used to represent relationships between objects. Unlike trees, graphs do not follow a strict parent-child hierarchy.

There is a deeply ingrained reflex in enterprise software development, particularly in the Java world: the moment you write a service class, you also write an interface for it. It feels almost automatic.

Three weeks of work. A fully functional AI chatbot, integrated into his company’s internal portal, built on top of GPT-4, with a clean UI and a snappy response time. His manager was impressed just from the screenshots.