You will get high-quality RLHF dataset optimization & LLM prompt tuning


Project details
Transform Raw AI Outputs into High-Accuracy, Human-Grade Training Data
Is your AI model hallucinating, generating robotic text, or failing instruction compliance? High-performing LLMs depend on meticulous human feedback and precise dataset refinement.
I specialize in Reinforcement Learning from Human Feedback (RLHF), prompt engineering, and qualitative AI data annotation. I turn inconsistent model outputs into accurate, culturally aligned, gold-standard training datasets.
What I Offer:
Hallucination & Bias Removal: Auditing model responses to eliminate factual errors and unwanted bias.
Prompt Tuning & Alignment: Optimizing inputs/outputs for tone, formatting, logic, and intent.
Error Taxonomy & Logging: Categorizing failure points (tone shift, instruction failure, edge cases) into structured logs.
Format Compliance: Clean, validated datasets delivered in JSON, CSV, or custom schemas.
Workflow:
Dataset & Guideline Review
Sample Calibration
Optimization & Logging
Final Delivery
Strict guideline adherence, fast turnaround, and 100% NDA respect. Let's make your AI smarter!
Is your AI model hallucinating, generating robotic text, or failing instruction compliance? High-performing LLMs depend on meticulous human feedback and precise dataset refinement.
I specialize in Reinforcement Learning from Human Feedback (RLHF), prompt engineering, and qualitative AI data annotation. I turn inconsistent model outputs into accurate, culturally aligned, gold-standard training datasets.
What I Offer:
Hallucination & Bias Removal: Auditing model responses to eliminate factual errors and unwanted bias.
Prompt Tuning & Alignment: Optimizing inputs/outputs for tone, formatting, logic, and intent.
Error Taxonomy & Logging: Categorizing failure points (tone shift, instruction failure, edge cases) into structured logs.
Format Compliance: Clean, validated datasets delivered in JSON, CSV, or custom schemas.
Workflow:
Dataset & Guideline Review
Sample Calibration
Optimization & Logging
Final Delivery
Strict guideline adherence, fast turnaround, and 100% NDA respect. Let's make your AI smarter!
AI Algorithms
Large Language Model, Multimodal Large Language Model, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, AI Text-to-Speech, AI-Enhanced Classification, Conversational AI, Image Analysis, Image Recognition, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Synthetic Data GenerationAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, PyTorch, Word2vecAI Models
BERT, BLOOM, ChatGPT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$50
|
Standard
$120
|
Advanced
$250
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 days |
Number of Revisions | 1 | 2 | |
AI Model Integration | - | - | - |
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | - | ||
Model Monitoring | - | - | - |
Model Testing & Optimization | |||
Model Tuning | - | ||
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$20 - $80
Additional Revision
+$20
Custom Dataset Schema Export (JSON/CSV)
+$30Frequently asked questions
About Baturalp
AI Data Operations Specialist | RLHF & Data Annotation Expert
Izmir, Turkey - 1:26 pm local time
High-quality LLM performance and model accuracy depend entirely on flawless data. In a fast-paced digital landscape that demands meticulous attention to detail, I help AI developers, tech startups, and data agencies refine their datasets and optimize their training pipelines. My work is where strict data analysis meets human-in-the-loop precision.
What I Bring to Your Project:
1. AI Training & Data Excellence (RLHF & Annotation):
I specialize in high-accuracy data labeling and Reinforcement Learning from Human Feedback (RLHF). Whether it's text tokenization, image bounding boxes, prompt engineering quality checks, or fine-tuning AI model responses to sound natural and reliable, I ensure your datasets are flawless and bias-free.
2. AI Content Humanizing & Quality Assurance (QA):
I transform robotic, generic, or hallucinated AI drafts into engaging, high-quality, and factually correct data. I review model outputs to catch nuances, logical flaws, and contextual errors that algorithms miss, directly improving dataset health.
3. Strict Guidelines & Zero-Latency Execution:
I treat your annotation guidelines, edge cases, and formatting structures as absolute rules. Backed by a high-performance MacBook and Samsung S25 Ultra ecosystem, I handle large-scale data tasks and high-volume text analysis with zero lag and exceptional speed.
4. Continuous Pipeline Improvement:
I don't just complete labeling tasks; I actively analyze data workflows to suggest better prompt structures, identify consistency gaps, and help scale your AI training operations efficiently.
A Note on Privacy & NDAs:
I highly respect client privacy and adhere strictly to NDAs, which is why I do not publicly share proprietary datasets or past client materials. Instead, I prefer to demonstrate my capabilities live and risk-free.
Let's collaborate: Drop a message in my inbox with a short annotation guidelines sheet, a test prompt, or a sample dataset, and I will deliver a free sample right away so you can verify my precision first-hand!
Steps for completing your project
After purchasing the project, send requirements so Baturalp can start the project.
Delivery time starts when Baturalp receives requirements from you.
Baturalp works on your project following the steps below.
Revisions may occur after the delivery date.
Dataset & Guideline Review
I review your raw prompts, AI outputs, and guidelines to ensure full project clarity.
Sample Batch Calibration
I audit a small sample batch (3-5 items) and align on evaluation criteria before full execution.