Top 50 LLM Interview Questions

In today’s newsletter:

• The Ultimate LLM Interview Handbook

• DeepCode: All-in-One Agentic Coding Framework

• The Complete Hands-On MLOps Repo

Reading time: 3 minutes.

The Ultimate LLM Interview Handbook

A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations.

If you are preparing for ML/AI Engineering interviews, this PDF will be useful. It includes 50 foundational LLM interview questions with clear explanations and examples to help you practice both applied and theoretical aspects.

Here is what is covers:

• Traditional ML basics and theory

• Model architectures and training methods

• Real-world case studies and applications

• Advanced LLM concepts and foundations

🔗 Check out the Handbook

DeepCode: All-in-One Agentic Coding Framework

DeepCode is an open source framework that automates end-to-end code generation through multi-agent orchestration.

It translates papers, prompts, and links into backend, frontend, and algorithm modules.

The framework provides three major automation pipelines:

1. Paper2Code - Academic ideas to runnable code

2. Text2Web - Plain text to production web frontends

3. Text2Backend - Requirements to scalable backend services

Key Features:

• Multi-modal input: PDFs, DOCX, URLs, and more
• Retrieval-augmented synthesis with CodeRAG
• Automated quality checks with tests, static analysis, and documentation
• Smart segmentation of long and complex papers

It’s 100% open source.

🔗 Check out the Github Repo

The Complete Hands-On MLOps Repo

This repository covers modern MLOps workflows end-to-end, including model building, monitoring, configurations, testing, packaging, deployment, and CI/CD.

Weekly roadmap (0–9):

• Week 0 - Project Setup: Data acquisition, processing, model declaration, training, and inference
• Week 1 - Model Monitoring: Logging, metrics, and visualization with Weights & Biases
• Week 2 - Configurations: Efficient configuration management with Hydra
• Week 3 - Data Version Control: Dataset and model versioning with DVC
• Week 4 - Model Packaging: Cross-platform compatibility with ONNX
• Week 5 - Model Packaging: Containerizing models and apps with Docker
• Week 6 - CI/CD: Automating ML workflows using GitHub Actions
• Week 7 - Container Registry: Managing images in AWS ECR
• Week 8 - Serverless Deployment: Deploying models with AWS Lambda
• Week 9 - Prediction Monitoring: Real-time monitoring using Kibana

Each step is project-based and hands-on: clone the repo, follow along, and implement a full-stack MLOps pipeline end-to-end.

🔗 Check out the Github Repo

That’s a Wrap

That’s all for today. Thank you for reading today’s edition. See you in the next issue with more AI Engineering insights.

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