The AI-Enabled Systems Engineering assessment measures the core technical competencies required by engineers and software developers to build and maintain AI-enabled business applications that are secure, scalable, highly-functional, and efficient. The test evaluates conceptual knowledge of designing, constructing, deploying, and managing modern AI systems, covering both foundational and advanced topics, including AI agents, multi-stage agent workflows, prompt engineering, integrated large language models (LLMs), and Retrieval-Augmented Generation (RAG), as well as integration concepts such as data engineering, APIs, and system guardrails.

The test measures skills across five key focus areas: Large Language Model (LLM) Integration and Prompt Engineering (covering prompt construction and optimization, LLM integration, and output management), Retrieval-Augmented Generation (RAG) Systems (covering retrieval pipeline design and optimization, and RAG system evaluation and quality assessment), AI Agents and Multi-Stage Agentic Workflows (covering agent tool use and orchestration, and agent memory and state management), API Integration, Data Engineering, and System Connectivity (covering API integration and service connectivity, and data ingestion, preprocessing, and pipeline management), and AI System Guardrails, Security, and Responsible Deployment (covering input/output validation and content filtering, AI monitoring, access controls, and responsible deployment). Candidates respond to multiple choice and multiple correct answer questions, which are drawn from a large question pool and may include individual time limits to discourage cheating and to improve the predictive performance of the assessment.

Topics covered by this test include:

Following completion, the test produces a Candidate Selection Report that includes an overall score and detailed scores for each attribute.

The Candidate Selection Report also provides expert interview questions that help you probe critical or low-scoring areas, along with a guide for noting your evaluation of the candidate's responses. Consistent use of an interview guide is an important part of gaining better candidate insights and making better hiring decisions.

Note that while the AI-Enabled Systems Engineering assessment is a useful and efficient instrument for confirming a base level of skill or knowledge about this important topic, it is not intended to pinpoint the exact level of candidate or skill. We recommend that skills tests, like this one, be used in parallel with other measurements, such as cognitive ability, job fit, and behavioral history. Each different type of test provides a valuable piece of information you can use when evaluating potential job performance.