How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Meta during technical interviews.

Build a Mistral-powered RAG agent | Meta Interview Question

Quick Overview

This question evaluates a candidate's ability to design and implement a retrieval-augmented generation (RAG) system using a large language model API, including document ingestion, chunking, vector indexing and retrieval, streaming completions, service interfaces (CLI and HTTP), and resilience features like retries, persistence, and structured error handling. Commonly asked in the ML System Design domain to assess practical engineering and architectural reasoning, it tests API integration, production-readiness (configuration, error handling, persistence, and testing) and emphasizes practical application with conceptual understanding of retrieval trade-offs.

Build a Minimal RAG Tool Using the Mistral API

Context

You have an API token and need to implement a small retrieval-augmented generation (RAG) tool in Python that can answer questions over a local folder of Markdown and PDF files using the Mistral API. The tool should support both a CLI and an HTTP server.

Requirements

Implement document ingestion, chunking, and an in-memory vector index for retrieval.
Provide a CLI with commands:
- index <path>
- ask <question>
- serve (HTTP server exposing a /chat endpoint)
Call chat/completions with streaming; include the top-k retrieved chunks in the prompt and return source citations.
Add exponential backoff/retries for 429 and timeouts, plus structured error handling.
Configure via environment variables for API key, model names, and ports.
Include a README with setup steps and minimal tests.
Briefly explain your retrieval algorithm choices and a quick way to evaluate answer quality.

Assumptions

Language: Python 3.10+.
Use the Mistral HTTP API directly to avoid client-library version mismatch.
You may persist the built index to disk so that ask and serve can reuse it across processes, while the core index data structure remains in-memory when serving queries.
Supported file types: .md and .pdf.

Quick Overview

Requirements

Implement document ingestion, chunking, and an in-memory vector index for retrieval.

Provide a CLI with commands:

index <path>
ask <question>
serve (HTTP server exposing a /chat endpoint)

Call chat/completions with streaming; include the top-k retrieved chunks in the prompt and return source citations.

Add exponential backoff/retries for 429 and timeouts, plus structured error handling.

Configure via environment variables for API key, model names, and ports.

Include a README with setup steps and minimal tests.

Briefly explain your retrieval algorithm choices and a quick way to evaluate answer quality.

Assumptions

Language: Python 3.10+.

Use the Mistral HTTP API directly to avoid client-library version mismatch.

You may persist the built index to disk so that ask and serve can reuse it across processes, while the core index data structure remains in-memory when serving queries.

Supported file types: .md and .pdf.

Build a Mistral-powered RAG agent

Quick Overview

Build a Minimal RAG Tool Using the Mistral API

Context

Requirements

Assumptions

Solution

Comments (0)

Build a Mistral-powered RAG agent

Quick Overview

Build a Minimal RAG Tool Using the Mistral API

Context

Requirements

Assumptions

Solution

Comments (0)