Robin Lindgren
AI Tutor — STEM Prompting & Review
Experience
AI Tutor — STEM Prompting & Review
Mindrift
- Authored prompts and evaluation rubrics for math/physics/CS; required clear step-by-step solutions, unit handling, and error-explanation notes.
- Reviewed model outputs for correctness and reasoning quality; created calibration sets and difficulty bands.
AI Data Annotator — Multimodal Labeling & QA
RWS (TrainAI)
- Labeled text/image data to evolving schemas; stabilized label taxonomies; maintained rationale notes and edge-case logs.
AI Linguistic Trainer (Swedish/English) — Criteria & Evaluation Design
Outlier AI
- Built scoring criteria for helpfulness, safety, factuality, and style; turned policy into example-led guidelines.
- Wrote adversarial prompts to probe ambiguity, safety, and factual precision; tuned thresholds to raise rater agreement.
Content Creator — Swedish (Chaya Project)
TransPerfect / DataForce
- Produced 1,000+ Swedish SMS/email samples; enforced strict style/safety guidelines.
- Result: 98.85% QA pass across 815 reviewed tasks.
Swedish AI Linguist & Trainer
DataAnnotation.tech
- Reviewed/refined model outputs across registers; designed task-specific rubrics; led small calibration passes.
Industries Experience
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Experienced in Information Technology (4 years), Professional Services (3 years), and Education (1 year).
Business Areas Experience
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Experienced in Quality Assurance (4 years), Research and Development (4 years), and Marketing (3 years).
Summary
LLM Trainer & Reasoning Specialist with 3+ years shaping high-fidelity prompts, evaluation rubrics, and gold-standard benchmarks across science/technology, legal principles, health & lifestyle, and multilingual use cases. I translate complex, policy-heavy instructions into clear, auditable workflows—including rationale notes, decision trees, inter-rater calibration sets, and defect taxonomies—that increase agreement, reduce rework, and raise throughput.
Proven record of quality at scale: 98.85% audited QA pass across 815 reviewed tasks, consistent SLA delivery in fully remote, fast-iteration environments. Strengths include reasoning-first prompt design (tiered variants, constraints, uncertainty language), evaluation operations (analytic/holistic rubrics, partial-credit logic, severity tagging, AQL spot checks), and safety/factuality governance (bias/fairness screens, non-advice framing, evidence-bounded prompts).
I partner closely with research and evaluation leads to convert model error analyses into derived prompts, adversarial test sets, clearer acceptance criteria, and versioned SOPs with complete audit trails. Tool-fluent with enterprise annotation and QA platforms; meticulous about metadata hygiene, template reuse, and documentation that scales across raters and projects.
Skills
Prompt Development (Reasoning-first): Craft Structured Prompts That Elicit Step-wise Reasoning; Write Tiered Variants (Basic → Advanced) With Clarity, Context, And Explicit Constraints.
Breadth Across Domains: Physics, Cybersecurity, Fitness, Automotive, Environment, Legal Principles; Plus Self-help/advice, Animals, Acg, Art, Books/reading.
Evaluation & Research Collaboration: Build Gold Standards, Define Scoring Rubrics (Holistic + Analytic), Run Inter-rater Calibration, And Iterate From Model Error Analyses.
Quality & Safety: Fact-checking Workflows, Citation Prompts, Boundary/adversarial Cases, Bias/safety Screens, And Minimal-edit Corrections.
Documentation: Versioned Sops, Rationale Logs, Error Taxonomies, Reproducible Templates For Prompt Suites And Benchmark Sets.
Physics (Mechanics & Em): Concept-scaffold Prompts (Givens → Unknowns → Formula Selection → Unit Consistency); Distractor-aware Mc Variants; Free-response With Rubric Describing Partial Credit And Common Slips (Sign Errors, Vector Decomposition, Sig-figs).
Cybersecurity: Threat-model Prompts (Stride/mitre Mapping), Log-snippet Analysis (Ioc Extraction), Secure Defaults/least-privilege Checklists, And Incident Synopsis Prompts With Evidence Tags And Scope Limits.
Fitness & Health: Evidence-bounded Prompts That Require Citing Guideline Sources; Progressive Overload Planning; Contraindication Checks And Scope-of-practice Guardrails (Non-diagnostic Framing).
Automotive: Troubleshooting Prompts Using Symptom Trees; Obd-ii Code Reasoning; Maintenance Schedules By Climate/load Profile; Safety Caveats.
Environment: Lifecycle Analysis Frames; Emissions Trade-off Calculators; Policy-vs-practice Prompts With Regional Variability And Uncertainty Notes.
Legal Principles (Non-advice): Issue-rule-application-conclusion (Irac) Skeletons For General Principles; Jurisdiction Flags; Cautionary “Not Legal Advice” Scaffolds; Case-fact Abstraction Prompts.
Built Benchmark Sets With Clear Acceptance Criteria, Partial-credit Rules, And Failure Modes; Logged Disagreements And Resolution Notes.
Ran Inter-rater Calibration With Seed Sets; Tracked Agreement Metrics; Iterated Instructions To Reduce Variance.
Partnered With Research Leads To Turn Model Misses Into Derived Prompt Variants (Counterfactuals, Negations, Distractor Density Controls).
Tools & Methods: Openai/anthropic/gemini Prompting; Json/csv Datasets; Google Sheets/docs For Rubrics; Labelbox/prodigy/cvat; Light Python For Validation; Markdown Templates; Fact-check Workflows.
Languages
Education
Stensund Folkhögskola
Graduated with Distinction, Top 5% · Behavioral Science Program · Trosa, Sweden
Profile
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