Prompt Engineering Experts in Dortmund

• Developed and optimised structured support workflows and evaluation procedures, applying consistent quality standards across high-volume operational tasks.
• Monitored performance metrics to identify systemic issues and drive targeted improvements — a skill directly transferable to LLM performance metric analysis.
• Managed escalations and maintained high accuracy and satisfaction standards in a fully asynchronous, remote-first environment.

Conventional Paradigm Test (CPT) – AI Evaluation & LLM Research

Development of an experimental evaluation approach to examine “paradigmatic closure” in Large Language Models — that is, the question of how far LLMs can recognize the basic assumptions, values, and limits of the paradigms within which they generate answers.

Design and testing of an additional approach to classic AI benchmarks that does not primarily measure factual correctness or task performance, but instead examines a model’s ability to recognize alternative perspectives, make implicit assumptions visible, and reflect on the limits of its own answer or interpretation framework.

Focus areas: development of evaluation criteria and test questions · LLM evaluation and comparative model analysis · prompt and response analysis · qualitative classification of model answers · study of epistemic compression and value leakage · benchmark and literature research · development of structured assessment and analysis methods

As part of CPT, existing AI evaluation approaches and benchmarks were analyzed, and a minimalist test protocol was developed that classifies model answers by response patterns such as DIRECT, CLARIFY, PLURALIST, REFUSE, and META-AWARE. TruthfulQA was used as the basis for experimental application and comparison with existing reference answers.

Technologies & Methods: Large Language Models (LLMs) · Generative AI · Prompt Engineering · AI Evaluation · TruthfulQA · Benchmark Analysis · Human-in-the-Loop Evaluation · Qualitative Content Analysis · Research & Literature Review

• Development of an automated job-scouting workflow for the aggregation and LLM-based evaluation of job postings from RSS feeds and APIs.
• Development of a Telegram bot for voice and text messages, featuring LLM-supported processing, summarization, and structured JSON output.
• Implementation of a personal task-planning assistant using n8n, OpenRouter, and Supabase for automated daily and knowledge organization.
• Development of LLM chatbots for structured knowledge retrieval utilizing prompt constraints, guardrails, and forced output formats.

• Led the 12-week training program to become an AI and automation specialist
• Created training materials & video courses on "AI Fundamentals", "Working with ChatGPT", "Prompt Engineering", "AI Agents with N8N", "Automations with Make.com" & AI-generated marketing content (images, videos, blog posts & social media posts)
• Conducted workshops on the above topics
• Tools: Loom, N8N, Make.com, ChatGPT, Midjourney, Gamma.app, Canva, Kling AI, Magnific AI
• Result: Participants learn the technical and business know-how to successfully build a business as AI and automation specialists

• Setting up new desktop computers
• Setting up a VPS for automation, databases and chat interface
• Connecting to Azure AI Foundry
• Developing various n8n workflows for email, social media and document management
• Implementing a QA system (backups, error handling, HITL)
• Setting up and optimizing an inbound voice agent

• Conduct structured evaluation of LLM outputs using Content Review Standards (CRS) and AI safety frameworks.
• Assess responses across high-risk domains including violence and criminal facilitation.
• Assess responses across high-risk domains including hate speech and harassment.
• Assess responses across high-risk domains including suicide and self-harm.
• Assess responses across high-risk domains including regulated advice (medical, legal, financial).
• Assess responses across high-risk domains including misinformation and fabricated claims.
• Assess responses across high-risk domains including defamation and intellectual property.
• Assess responses across high-risk domains including child safety and sexual exploitation.
• Assess responses across high-risk domains including political and sensitive content.
• Apply youth-protection and age-appropriateness guidelines to prevent unsafe facilitation or restricted substance guidance.
• Classify prompts as adversarial, borderline, or benign based on contextual intent and risk analysis.
• Evaluate model behavior types including correct refusal, partial refusal, over-refusal, under-refusal, improper compliance, and ignorance-based outputs.
• Identify policy misapplications and user-intent misinterpretation patterns.
• Designed structured adversarial and borderline multi-turn conversation flows to stress-test AI boundary enforcement and reasoning stability.
• Identified failure modes including hallucination, unsafe compliance, excessive refusal, contextual drift, and inconsistent safety logic.
• Applied a structured four-dimension evaluation rubric covering accuracy & safety, relevance & completeness, clarity & structure, and tone & appropriateness.
• Provided structured feedback supporting supervised fine-tuning and reinforcement learning from human feedback processes.
• Rewrote unsafe or misaligned outputs into compliant, accurate, and helpful responses.
• Performed Persian ↔ English translation and translation validation of AI-generated content.
• Assessed semantic accuracy, contextual consistency, and safety alignment across languages.
• Identified mistranslations, cultural nuance issues, and cross-lingual policy inconsistencies.
• Recognized with the Above & Beyond Award – Q3 2025 for exceeding quality standards and embracing innovation.