You will get a reproducible LLM evaluation audit and reliability benchmark
Project details
You will get a reproducible LLM evaluation audit designed to move beyond subjective prompt testing with measurable evidence.
I will build a structured evaluation suite around your use case, run controlled tests, score model outputs against explicit acceptance criteria, classify failure modes, and deliver reproducible metrics with an audit report.
The evaluation can cover issues such as hallucinations, missing required information, refusal behavior, formatting errors, instruction following, and regressions between prompts, models, or configurations.
Depending on the selected tier, deliverables may include the evaluation dataset, raw results, metrics, failure cases, reusable Python code, and a documented handoff.
I focus on auditability: failure cases remain inspectable, methodology and limitations are documented, and infrastructure/API errors are separated from model-quality findings. No fabricated benchmark claims.
I will build a structured evaluation suite around your use case, run controlled tests, score model outputs against explicit acceptance criteria, classify failure modes, and deliver reproducible metrics with an audit report.
The evaluation can cover issues such as hallucinations, missing required information, refusal behavior, formatting errors, instruction following, and regressions between prompts, models, or configurations.
Depending on the selected tier, deliverables may include the evaluation dataset, raw results, metrics, failure cases, reusable Python code, and a documented handoff.
I focus on auditability: failure cases remain inspectable, methodology and limitations are documented, and infrastructure/API errors are separated from model-quality findings. No fabricated benchmark claims.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPT, LLaMAWhat's included
| Service Tiers |
Starter
$79
|
Standard
$199
|
Advanced
$399
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 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.
Additional 25 Evaluation Cases
(+ 1 Day)
+$60
Additional Model or Config
(+ 1 Day)
+$50
Extended Failure Analysis
(+ 1 Day)
+$75Frequently asked questions
About Zihao
LLM Engineer | Evaluation, PyTorch & vLLM
Shanghai, China - 8:20 am local time
benchmarks instead of relying only on subjective prompt testing.
I can help with:
• LLM evaluation and regression testing
• LLM reliability/debugging
• open-source model deployment with vLLM
• Python/PyTorch backend engineering
Stack:
Python, PyTorch, Hugging Face, vLLM, FastAPI, Docker, Linux.
I prefer tightly scoped projects with measurable acceptance criteria,
reproducible environments and documented results.
Steps for completing your project
After purchasing the project, send requirements so Zihao can start the project.
Delivery time starts when Zihao receives requirements from you.
Zihao works on your project following the steps below.
Revisions may occur after the delivery date.
Define the evaluation protocol
I review your use case, reference material, known failures, and success criteria, then define the evaluation scope and acceptance rules.
Build the evaluation suite
Build the evaluation suite I create or refine structured test cases covering expected behavior, edge cases, reliability risks, and the agreed evaluation criteria.