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Mithilesh Kumar on Muck Rack

Mithilesh Kumar

As seen in: Medium, SAGE Publishing, Outlook (India), Financial Express, DEV Community, Canadian Journal of Infectious Diseases & Medical Microbiology, International Journal of Antennas and Propagation, Dialnet - Universidad de La Rioja, Economic and Political Weekly, The Cureus Journal of Medical Science and
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Articles

The RAG Pipeline I Wouldn't Build the Same Way Twice

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By Mithilesh Kumar
| DEV Community
Subtitle: A practical look at failure modes, query routing, hybrid retrieval, and knowing when agentic workflows are actually worth the complexity. The Illusion of a Perfect Prototype Every developer's first naive RAG system feels like magic. You chunk a few PDFs, pass them through an embedding model, save the vectors to a database, and hook up a top-k similarity search to an LLM. It takes 50 lines of Python, runs in a couple of seconds, and answers basic questions surprisingly well.
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Why Basic RAG Fails in Production and How Adaptive Query Routing Fixes It

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By Mithilesh Kumar
| DEV Community
Most developers build Retrieval-Augmented Generation (RAG) pipelines assuming every user query needs a vector search. In production, this naive approach fails in three distinct scenarios: Simple Queries: "Hi", "Who created this bot?", or general knowledge queries don't need expensive vector database lookups. Ambiguous Queries: Vague user questions lead to noisy retrieval, diluting the LLM's context window with irrelevant chunks.
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Building Scalable Multi-Agent Workflows & RAG Pipelines with LangGraph and FastAPI

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By Mithilesh Kumar
| DEV Community
Building fully autonomous AI systems requires moving beyond simple linear prompts to robust, stateful agentic workflows. In this article, I share my core architecture for engineering Multi-Agent Systems and Retrieval-Augmented Generation (RAG) pipelines using LangGraph, FastAPI, and Python. Why Agentic Workflows and LangGraph? Traditional LLM applications often struggle with complex, multi-step execution paths.
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