You will get Custom AI Evaluation Benchmark Design and Task Authoring
Project details
I design custom evaluation benchmarks that tell you whether your AI model actually works for your use case, not just whether it scores well on public leaderboards. Each task comes with a test harness that runs deterministically, a scoring rubric calibrated against frontier models, and difficulty gating that ensures the benchmark separates strong models from weak ones. I have built evaluation systems across coding agents (SWE-bench style Docker sandboxed tasks), document grounded writing (pairwise comparison of Gemini, GPT, and Claude across finance, education, and marketing), game agent evaluation (Roblox Studio harnesses with Luau eval scripts), and security fuzzing (C/C++ crash repair benchmarks). I also design the meta-evaluation layer: catching when your LLM judge disagrees with itself, when your rubric measures the wrong thing, and when your pass rate shifts because of prompt phrasing rather than actual model quality. You get a benchmark that holds up under scrutiny, not one that looks good until someone asks how it was validated.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI-Enhanced Classification, Conversational AI, Natural Language Understanding, Sentiment Analysis, Synthetic Data GenerationAI Development Language
PythonAI Tools
Gradio, Hugging FaceAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$500
|
Standard
$2,000
|
Advanced
$4,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
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
+$100 - $900
Additional Revision
+$30Frequently asked questions
About Kunal
AI Evaluation Engineer | LLM Benchmark Design & Red-Teaming
Hyderabad, India - 1:24 am local time
What I deliver:
• LLM red-teaming and model weakness analysis (22-family taxonomy)
• RLHF dataset authoring and quality review
• SWE-bench-style coding benchmark design with machine-verifiable test suites
• Multi-hop reasoning task design that exposes model overconfidence
• Prompt engineering and evaluation rubric development
• Pairwise model comparison across GPT, Claude, and Gemini families
Languages: Python, Go, Rust, C/C++, Lua, Bash
Infrastructure: Docker, Harbor runners, pytest harnesses, CI/CD pipelines
Previously delivered for: Snorkel AI, AfterQuery, Xelron AI, Labelbox
Steps for completing your project
After purchasing the project, send requirements so Kunal can start the project.
Delivery time starts when Kunal receives requirements from you.
Kunal works on your project following the steps below.
Revisions may occur after the delivery date.
Scope review
I analyze your requirements, SOPs, and target models to define benchmark architecture
Task authoring
I build evaluation tasks with test harnesses, rubrics, and reference solutions