Introduction: Why Entry-Level AI Hiring Feels Broken
For graduates aspiring to enter AI-driven careers in the Netherlands, the path is narrowing. According to NL Times, only 9% of open tech roles are now aimed at recent graduates—down from 14% just a year ago (NL Times, 2025). This steep drop highlights how early-career candidates, including those aiming for roles like AI Prompt Engineer, face fewer opportunities at precisely the moment when demand for AI expertise is skyrocketing.
Employers, meanwhile, struggle with a paradox. On one hand, they need specialists who can design and refine prompts for AI systems, ensuring accuracy, reliability, and ethical use. On the other, the flood of applications makes it difficult to distinguish who can actually perform day-one tasks. Traditional filters—CVs, degree prestige, or vague portfolio claims—rarely provide clear evidence of prompt design skills.
Universities are equally strained. AI tools and frameworks evolve almost monthly, and curricula can lag behind. Students graduate with partial exposure but lack practice in applying generative AI tools to real-world workflows.
The result? Mismatch, wasted opportunity, and frustration—for candidates, employers, and educators alike.
Talantir’s perspective is simple: the solution lies in evaluating capability through real tasks, not just paper qualifications.
Current Frictions in Early-Career AI Hiring
Application Volume
With fewer graduate-focused roles available, competition intensifies. Reports show graduate-level vacancies in the Netherlands attract dozens, often hundreds, of applicants per position (NL Times, 2025). Employers, inundated with CVs, rely on automated filters to manage the load. Unfortunately, this often screens out promising candidates who lack certain keywords but have the actual ability to craft effective prompts.
Time to Hire
Across Europe, the average hiring process stretches into weeks, sometimes months. In fields like AI, this lag is unsustainable. Generative AI technologies evolve rapidly—tools like GPT-4o, Claude, and Llama update every quarter. A 4–6 week time-to-hire (as UK benchmarks suggest StandOut CV, 2023) risks leaving roles vacant while the technology landscape shifts underfoot.
Skills Mismatch
Employers consistently cite mismatches between graduate readiness and job demands. The CIPD Labour Market Outlook notes that over half of employers face difficulty finding candidates with the right skills (CIPD, 2023). For AI Prompt Engineers, this mismatch often lies in the practical: students may understand AI models conceptually but lack applied experience in prompt design, testing, and iteration.
Poor Signal Quality
CVs and cover letters say little about how a graduate will actually design, refine, and test AI prompts. A line reading “familiar with generative AI” offers no proof of skill. Interviews often privilege candidates who speak confidently but don’t reveal how they solve real prompt engineering challenges, like mitigating bias or improving model consistency.
Assessment Drift
Many entry-level AI hiring processes default to abstract logic tests or generic coding challenges. While useful for measuring aptitude, these don’t reflect the work of an AI Prompt Engineer—balancing clarity, context, and creativity in a live AI environment. This disconnect frustrates candidates and leaves employers without reliable signals.
Why AI Prompt Engineer Roles Are Hard to Evaluate
AI Prompt Engineer is still an emerging role, which adds complexity:
- Unclear job titles: Some ads say “AI Prompt Engineer,” others list “AI Specialist,” “LLM Engineer,” or even “AI Content Designer.” Each varies in scope, confusing both applicants and recruiters.
- Hybrid skill mix: Prompt engineering blends technical (understanding model behavior), linguistic (clarity and context), and ethical (bias mitigation, compliance) skills. Few graduates have exposure to all three.
- Rapidly shifting tools: Prompting techniques that worked in 2023 may be outdated by 2025. Staying current requires practice, not just theory.
- High stakes: Poor prompt design can lead to inaccurate outputs, reputational risks, or even compliance issues under EU AI regulations. Employers are risk-averse, narrowing pipelines further.
These challenges mean early-career candidates are judged by proxies—university prestige, certifications, or portfolio projects—rather than evidence of what they can actually do.
The Alternative: Work-Sample Evaluation
Instead of relying on abstract CVs or long interviews, employers could assess graduates through short, realistic work samples. These tasks simulate day-one responsibilities and reveal practical ability.
For AI Prompt Engineers, work-sample evaluations might include:
- Crafting a set of prompts to generate consistent, bias-free outputs from a model
- Iteratively refining a prompt to improve model accuracy on a given task
- Designing a system message to guide tone and style for a customer-facing chatbot
- Documenting prompt-testing results for a non-technical stakeholder
Such tasks don’t require days of unpaid labor—they can be completed in 30–60 minutes—but they demonstrate much more than a polished CV.
Why this approach matters:
- For students: It’s fairer—they prove ability through practice, even without elite internships.
- For employers: It surfaces motivated candidates who can already demonstrate core capabilities.
- For universities: It offers alignment—coursework can be adapted to reflect industry-relevant tasks.
Research in organizational psychology shows work-sample tests consistently outperform other predictors of job performance. For a nascent role like AI Prompt Engineer, they offer the clarity both sides need.
Talantir’s Perspective: Capability-First for AI Roles
At Talantir, our model is built on capability-first hiring and readiness. Students engage in structured roadmaps built around real-world cases, then enter challenges aligned to employer needs.
For AI Prompt Engineer roles, this could look like:
- Roadmap cases: students experiment with real prompts across tools like GPT, Anthropic, or open-source LLMs, learning how small changes affect outputs.
- Milestones: they complete projects combining technical, linguistic, and ethical dimensions—for example, building a mini knowledge assistant and testing it for bias.
- Challenges: employers launch targeted tasks, such as refining prompts for accuracy or drafting documentation for business stakeholders.
For students: this approach builds clarity on whether AI prompting suits their skills. They graduate with a portfolio showcasing evidence—not just aspirations.
For employers: instead of filtering 200+ CVs, they review deep profiles showing how candidates approached real tasks. Profiles include structured abstracts summarising each student’s problem-solving approach.
For universities: prompt-engineering roadmaps can be embedded with minimal lift, helping institutions keep pace with evolving AI skills demand. Career services also gain analytics on readiness, supporting curriculum design and employer engagement.
By centring on real work, not worksheets, Talantir reduces noise and strengthens trust in early-career AI hiring.
Conclusion: What If We Evaluated Real Work, Not Promises?
The friction in early-career hiring for AI Prompt Engineers in the Netherlands is clear: too few entry-level roles, overwhelming application volumes, long timelines, mismatched skills, and unreliable signals. The result is lost opportunity for graduates and slower innovation for employers.
Work-sample evaluation offers a practical reset. By focusing on realistic, manageable tasks, we can create fairer, faster, and more reliable pathways into AI careers. Students demonstrate capability, employers gain confidence, and universities align teaching with industry needs.
What if we evaluated real work, not promises? That’s the question Talantir puts at the heart of early-career hiring.
Explore how work-sample evaluation can reset early-career hiring standards.