{
  "id": 17642806,
  "title": "What do coding interviews miss about real-world Applied AI and Forward Deployed Engineering?",
  "url": "https://urgent.news/2026/11/01/what-do-coding-interviews-miss-about-real-world-applied-ai-and",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-01T17:18:17.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/chuffedlyworks/what-do-coding-interviews-miss-about-real-world-applied-ai-and-forward-deployed-engineering-3ol7"
  },
  "original_language": "en",
  "account": "Engineering interview preparation often revolves around solving well-defined problems with clearly specified inputs and outputs. However, this is at odds with the reality of customer-facing engineering. Consider a client request such as, \"We have thousands of support tickets. Can you use AI to identify the important ones and route them to the right teams?\" Before diving into coding, one would need to comprehend what determines a ticket's importance, the consequences of the system's mistakes, how success can be measured when labels are incomplete, the optimal points for human intervention when an automated decision is required, and strategies for detecting problems post-deployment. The true essence of engineering work often lies in unearthing the requirements, devising evaluation methods, and determining the boundaries of automation.\n\nDrawing from experiences in applied AI, solutions engineering, or forward-deployed roles, what skills prove to be the most challenging to evaluate through standard coding exercises? Which practical deployment or customer-integration challenges would make for an effective real-world exercise? If you have prepared for these roles, what aspects of existing resources seemed to fall short? Please refrain from disclosing any confidential interview questions or employer-specific information. This inquiry is an academic exploration, with no intent to sell or promote any educational product.",
  "summary": "A lot of engineering interview preparation focuses on solving a well-defined problem with clearly specified inputs and outputs. But customer-facing engineering rarely starts that way. Imagine receiving a client request like: “We have thousands of support tickets. Can you use AI to identify the important ones and route them to the right teams?” Before writing code, I’d want to understand: What…",
  "key_points": [
    "Real-world AI involves understanding requirements and defining success metrics.",
    "Coding interviews lack context of customer feedback and post-deployment monitoring.",
    "Practical exercises should focus on deployment challenges and human-AI collaboration."
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}