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4 articles
LangGraph models an agent as nodes, edges and a shared state object. That structure buys three things a plain agent loop cannot: a place to put state, control over what happens next, and the ability to resume a run that was interrupted.
Rajiv Kumar·Aug 19, 2026·9 min read
When a RAG system gives a wrong answer, the instinct is to change the prompt. Almost always the right passage was never retrieved. Chunking, hybrid search, filters and semantic ranking are the levers that actually move quality.
Rajiv Kumar·Jun 22, 2026·11 min read
RAG, evaluation, agents, cost and failure handling are where generative-AI interviews should go. These questions have follow-ups, and the strong answers are about engineering rather than about models.
Rajiv Kumar·May 20, 2026·6 min read
The questions that come up whenever a team starts building with language models: why it makes things up, whether to fine-tune, how much context to send, what it will cost, and how to know whether a change made things better.
Rajiv Kumar·May 6, 2026·6 min read
