🧠 LLM Engineering Pre-Assessment
Goal: Assess understanding of core AI/ML concepts.
Questions:
What is a neural network, and how does it learn?
Define parameters vs. hyperparameters.
What is overfitting, and how do you prevent it?
What is the difference between supervised, unsupervised, and reinforcement learning?
Goal: Evaluate understanding of how LLMs work.
Questions:
What is a transformer architecture?
Explain self-attention and its role in transformers.
What are tokenization and embedding?
What is the difference between GPT, BERT, and T5?
How do models handle context length?
Goal: Assess ability to apply LLMs in code or tools.
Tasks:
Write a Python snippet that calls an OpenAI API to get a text completion.
Build a simple chatbot using LangChain or LlamaIndex.
Create a small RAG pipeline (Retrieval-Augmented Generation).
Load a local vector database (like FAISS, Chroma, or Pinecone).
Deploy a Streamlit or Hugging Face Spaces demo.
Goal: Measure understanding of Retrieval-Augmented Generation.
Questions:
What are the key components of a RAG system?
Explain how document embedding and retrieval work.
What’s the difference between retrieval and generation stages?
What are common challenges in RAG (e.g., hallucination, relevance)?
How do you evaluate the performance of a RAG system?
Goal: Assess ability to control model behavior.
Tasks:
Write a zero-shot, few-shot, and chain-of-thought prompt example.
Turn a vague user question into a structured LLM prompt.
Design a system prompt for an AI Business Assistant agent.
What’s the difference between prompt templates and dynamic context injection?
Goal: Evaluate ability to build and structure real-world LLM apps.
Scenarios:
Design a multi-agent architecture for an AI Business Assistant.
Explain how you would handle memory, context management, and user sessions.
How would you use Kubernetes + Helm to deploy scalable LLM services?
How can you make your app production-grade (monitoring, logging, cost control)?
Goal: Assess awareness of responsible AI practices.
Questions:
What are risks of bias and misinformation in LLMs?
How do you implement content filtering and moderation?
What does data privacy mean in an AI app context?
Goal: Evaluate applied understanding.
Challenge Example:
Build a simple AI Knowledge Assistant using RAG:
Use PDF documents as your knowledge base.
Embed them with OpenAI or SentenceTransformers.
Store in FAISS or Chroma DB.
Query via Streamlit UI.
Deploy to Hugging Face Spaces or Streamlit Cloud.