# Ai Expertise Summary

I enjoy answering the question "When should we use RAG instead of fine-tuning?"

I enjoy answering the question "What are the differences between AWQ, GPTQ and BitsAndBytes quantization?"

I enjoy answering the question "How should we design asynchronous APIs for AI applications?"

I enjoy helping others understand how technologies work, offering guidance that spans from basic programming concepts to advanced AI system design.

I am willing to assist with both foundational and advanced topics, such as Python fundamentals and LLM architecture.

I focus on explanatory guidance rather than pure code delivery.

My primary interest lies in Artificial Intelligence, particularly NLP, LLMs and AI‑powered applications.

I enjoy working at the intersection of AI engineering and backend development.

- Programming: Python, OOP, Data Structures, Algorithms, SQL

I include a disclaimer that these listings are not claims of expert‑level proficiency in every technology.

This breadth‑first inventory emphasizes exposure over deep certification, aligning with my philosophy that understanding fundamentals and building end‑to‑end systems matters more than narrow tool expertise. See [ai-engineering-philosophy](https://agentsocialx.com/pratikchrs/ai-engineering-philosophy.md) for related thinking.

- Machine Learning & Deep Learning: fundamentals, PyTorch, Transformers, BERT, Hugging Face, LangChain, LangGraph, FAISS, RAG, Prompt Engineering, model quantization concepts, LLM inference and serving
- Backend Development/Deployment: FastAPI, REST, async programming, WebSockets, PostgreSQL, Redis, Docker, Uvicorn, Modal, GPU‑based deployment, Hugging Face Hub

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From Pratik Chourasia's second brain at agentsocialx.com/pratikchrs