Abhishek Chauhan

Bridging the gap between engineering velocity and architectural reliability.

LLM Testing RAG Validation AgenticAI Prompt Engineering MCP Data Generation

Validating reliability and safety in AI-driven workflows and RAG architectures.

Technical Strategy

Aligning roadmaps with business goals.

Engineering Enablement

Building tooling and culture for high-velocity delivery.

Cross-Team Influence

Establishing organization-wide standards.

LinkedIn Profile

End-to-End NLP Pipeline: Fine-Tune, Evaluate, Deploy, and Test a Foundation Model

A complete walkthrough of building a production NLP pipeline — PEFT fine-tuning, RAG, evaluation with FMEval and DeepEval, GitHub Actions CI/CD, AWS Bedrock deployment, and API testing via AWS API Gateway — using a courier company as the running example.

#NLP #PEFT #LoRA #RAG #FMEval #DeepEval #AWS Bedrock #GitHub Actions #LLM #Fine-Tuning #MLOps #Tutorial

FmEval: The Guide to Evaluating Your LLMs

A practical crash course on Amazon's FmEval library — what it is, why it matters, how to set it up, and how to evaluate LLM outputs across accuracy, toxicity, robustness, and bias without drowning in theory.

#LLM Evaluation #FmEval #Machine Learning #Generative AI #Python #Quality Engineering

Ragas: Evaluating Your RAG Pipeline

A practical crash course on Ragas — the framework that tells you whether your RAG pipeline is actually working or just confidently hallucinating. Covers all core metrics, test set generation, LlamaIndex integration, local LLMs, and how to diagnose what's actually broken.

#RAG #Ragas #LLM Evaluation #Retrieval Augmented Generation #Machine Learning #Python

DeepEval: Guide to Testing Your LLM Applications

A practical crash course on DeepEval — the pytest-style evaluation framework for LLMs. Covers installation, all core metrics, G-Eval for custom criteria, RAG evaluation, synthetic dataset generation, and CI/CD integration.

#LLM Evaluation #DeepEval #Machine Learning #Generative AI #Python #Quality Engineering #QE