Building AI-Powered Systems by Shubham Zende: RAG, LLMs, and Autonomous Workflows

Artificial Intelligence has evolved from standard machine learning models to dynamic generative systems capable of reasoning and automating complex workflows.

Retrieval-Augmented Generation (RAG) is one of the most effective patterns for providing Large Language Models (LLMs) with domain-specific knowledge. By converting documents into vector embeddings and querying vector databases (such as Pinecone or Milvus), we eliminate hallucinations and enable real-time factual responses.

When designing AI architectures, key considerations include chunking strategy, embedding quality, prompt context windows, and fallback mechanisms for API rate limits.

As AI continues to transform software engineering, building robust AI agents and automated pipelines will remain a core focus of my technical strategy.