Generative AI fundamentals
Transformers, tokenisation, attention, decoding, context windows, probabilistic generation, model families, capabilities, and limitations.
Generative AI engineer pre-employment test
Screen job-ready GenAI talent with a Generative AI Engineer Pre-Employment Test.
Evaluate LLMs, prompt engineering, retrieval-augmented generation, embeddings, fine-tuning, multimodal models, AI agents, evaluation, safety, and production GenAI engineering before technical interviews.
LLMs & prompt engineeringRAG, embeddings & vector searchFine-tuning & multimodal AIAgents, MLOps & safety
Skill signals
What this Generative AI pre-employment test helps you evaluate
Measure practical GenAI engineering ability through role-relevant questions, prompt and RAG scenarios, model-evaluation tasks, system-design challenges, automated scoring, and explainable candidate reports.
LLMs & architectures
GPT-style models, encoder-decoder systems, Llama, Mistral, Claude, Gemini, model selection, tokens, latency, and cost trade-offs.
Prompt engineering
Prompt patterns, system instructions, few-shot examples, chain-of-thought strategy, structured outputs, testing, and prompt optimisation.
RAG & embeddings
Embeddings, vector databases, chunking, retrieval, metadata filtering, hybrid search, re-ranking, grounding, and hallucination reduction.
Fine-tuning & PEFT
Supervised fine-tuning, instruction tuning, LoRA, QLoRA, adapters, datasets, evaluation, alignment, and fine-tuning trade-offs.
Multimodal GenAI
Vision-language models, image generation, speech, audio, captioning, document understanding, multimodal prompting, and evaluation.
AI agents & applications
Tool calling, planning, memory, workflows, orchestration, copilots, chatbots, autonomous agents, reliability, and real-world use cases.
MLOps, evaluation & safety
Model serving, observability, versioning, latency, cost monitoring, guardrails, red teaming, bias, privacy, governance, and responsible AI.
Assessment flow
A practical structure for fair Generative AI screening
Move candidates through a consistent, job-relevant pre-employment process with secure delivery, automated evaluation, and decision-ready GenAI skill reports.
01
Invite candidates
Send the Generative AI assessment by email or share a secure test link with applicants.
02
Run practical GenAI tasks
Candidates solve prompt, RAG, embedding, fine-tuning, agent, evaluation, safety, and production-system questions.
03
Auto-evaluate
Score correctness, prompt quality, retrieval design, model reasoning, evaluation choices, safety awareness, and practical trade-offs.
04
Review detailed reports
Compare candidates using skill breakdowns, question analysis, model-performance indicators, scorecards, and hiring recommendations.
Score breakdown
Example Generative AI Engineer score areas
Use cases
Where this pre-employment test fits best
Generative AI Engineer hiring
Validate LLM, prompt engineering, RAG, fine-tuning, agent, evaluation, safety, and deployment skills before interviews.
AI product and platform teams
Assess engineers building copilots, chatbots, search, content generation, enterprise RAG, automation, and agentic applications.
Graduate and campus hiring
Identify candidates with strong programming, AI foundations, experimentation skills, and practical GenAI learning potential.
Use structured tasks, automated evaluation, and explainable score reports to shortlist stronger Generative AI candidates with confidence.
Identify Generative AI Engineers who can build useful, reliable, and responsible AI applications.
Use structured tasks, automated evaluation, and clear reports to shortlist stronger engineering candidates faster.