RAG Systems in Production: Lessons Learned
Building a RAG chatbot from scratch, vector embedding optimization, and real-world retrieval challenges.
Building portable MRI technology at neuro42. Firmware, APIs, image processing, cloud infrastructure. Also building and improving agentic AI workflows. Based in San Francisco.
I am currently an Embedded Software Engineer at neuro42, where I help build software for portable MRI devices. Previously, I was at Tarana Wireless and did LLM research at Santa Clara University. I did my undergrad in Electrical Engineering in Gujarat, India, then got my Master's in ECE from Santa Clara University.
Outside of work, I'm building agentic AI workflows, playing tennis (16+ years and counting), and gaming way too much Counter-Strike, PUBG, and Sims.
Here are some technologies I have been working with:
neuro42, Inc.
Tarana Wireless, Inc.
Santa Clara University
Tag-N-Trac
Distributed LLM evaluation harness with content-addressed response caching, versioned judges, and interchangeable local/Ray execution backends. Re-scoring after a scorer fix costs $0.00 and takes under a second.
Emotion recognition from EEG brain signals using CNN and LSTM. FFT for feature extraction. LSTM gave best accuracy across train-test splits.
G070 board with meat probe thermocouple, EEPROM calibration, UI for target temps by meat type and doneness.
Haar Cascades for face detection, Caffe models for age/gender, custom model for emotion classification.
ARM Cortex with RS485 data streams, fiber optic links to remote display modules, GPS coordinate reporting.
UVM testbench with full functional coverage across SMOKE, 1-PORT, 4-PORT, and CONCURRENT tests.
Chatbot for students using Retrieval Augmented Generation with Elastic Search and optimized vector embeddings.
A 3-layer architecture separating directives, orchestration, and deterministic execution. LLMs handle decision-making while Python scripts handle reliable execution. Self-annealing error loops that fix scripts, update directives, and get stronger over time.
A test failed one time in five. The obvious fix, suggested by Claude, was wrong. Here's how a 30-line reproduction script found the real cause.
July 8, 2026 · 11 min readThe challenges of writing software for medical imaging devices, from real-time constraints to regulatory compliance.
Building a RAG chatbot from scratch, vector embedding optimization, and real-world retrieval challenges.
Why putting LLMs in charge of everything fails, and how a 3-layer architecture with deterministic execution fixes it.
Looking for my next role in embedded systems, medical devices, or forward deployed engineering. Let's connect.