Arash Nicoomanesh

Gemma 3n Edge AI for Support Bots

Low-memory, high-speed training on customer-support data

LLM Output Config & Guardrails

Master reasoning prompts and guardrails for reliable outputs

Few-Shot & Zero-Shot Learning Deep-Dive

Push LLMs beyond narrow fine-tuning.

In the age of large language models (LLMs), the ability to perform complex tasks with minimal data is revolutionizing how we approach artificial intelligence. Few-shot and zero-shot learning are two pivotal techniques that push the boundaries of machine learning, enabling models to generalize across domains and perform tasks they were not explicitly trained on. This article delves into these learning paradigms, explaining their origins, mechanisms, and real-world applications

Model Drift: A Survival Guide

Monitor & remediate production ML models

Fine-Tune Gemma-3 12B with Unsloth

End-to-end Unsloth & TRL workflow for customer service

In this article, I have dived into the technical intricacies of Unsloth and Gemma 3, showcasing their powerful features and how they can be leveraged together to build a highly optimized, fine-tuned model for any type of customer support assistant, whether it be a sophisticated chatbot, an intelligent agent, or an interactive FAQ system. I provided a step-by-step guide through the fine-tuning process, from data preparation to model deployment, highlighting best practices and practical considerations for achieving optimal performance in real-world customer service scenarios

Is RAG Dead in 2025?

Rethinking documentation Q&A with large-context LLMs.

Talk: Agentic RAG in Healthcare

15-min deck on self-evolving retrieval agents.