Akachukwu I.

Key Skills

Software

Label Studio

Prodigy

Other

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Task Types

Text Generation

Prompt + Response Writing (SFT)

Freelancer Overview

My experience in data labeling and AI training data is rooted in my work as a Prompt Engineer and LLM specialist. I have designed, tested, and refined thousands of prompt‑response pairs to improve model accuracy, reduce hallucinations, and optimize task success rates. Using few‑shot and chain‑of‑thought techniques, I’ve created reusable prompt libraries that effectively serve as structured training data for fine‑tuning LLMs. Additionally, I have performed A/B testing on prompt variants, analyzed output quality, and iteratively labeled “good” vs. “bad” responses to build cleaner training datasets for product teams. What sets me apart is my systems‑thinking approach. I don’t just label data I build workflows that automate quality checks and discrepancy resolution (e.g., maintaining 99.9% accuracy in high‑volume data entry). I’ve trained 10+ team members on best practices for prompt and response annotation. With certifications in Google Prompting Essentials and Microsoft Data Science, plus hands‑on experience using ChatGPT, Claude, and Gemini, I bridge the gap between raw training data and production‑ready AI performance.

Labeling Experience

LLM Prompt‑Response Pair Labeling & Quality Assurance

TextPrompt + Response Writing (SFT)

Authored high‑quality prompt‑response pairs for supervised fine‑tuning (SFT) of LLMs at Tata Groups. Scope: created 1,200+ original (prompt, response) pairs across domains including customer support, creative writing, summarization, and instruction‑following tasks. Each pair was written to demonstrate ideal model behavior: clear, safe, factual, and aligned with brand tone. Tasks performed: · Designing diverse prompt templates (zero‑shot, few‑shot, chain‑of‑thought). · Writing gold‑standard responses as target labels for SFT. · Iteratively refining pairs based on model output comparisons (using ChatGPT, Claude, Gemini). · Tagging each pair with metadata (difficulty, domain, toxicity check, factual grounding). Project size: solo contributor + QA reviewer, 6 months active writing. Quality measures: 100% human review of all responses, 98% first‑pass acceptance rate, weekly adversarial testing to catch edge cases.

2024 - Present

Education