You will get an LLM/RAG output quality audit with failure analysis and eval cases
Project details
I will review your LLM/RAG/output pipeline and identify why the AI produces weak, hallucinated, inconsistent, unsafe, or low-performing outputs.
This is not just a review of isolated answers or a one-off prompt rewrite. I inspect the pipeline behind the output: inputs, prompts, context assembly, retrieval/reranking, model behavior, SFT or fine-tuning data if relevant, output schema, post-processing, business rules, and feedback/evaluation signals.
You can use this for RAG, chatbots, document QA, AI-generated copy, recommendation explanations, structured LLM outputs, or other AI product features.
Depending on the package, you will receive a lightweight snapshot, structured failure analysis, evaluation cases, scoring rubric, and/or an evaluation workflow blueprint. The goal is to turn vague quality complaints into clear failure types, root causes, and prioritized fixes you can act on.
This is not just a review of isolated answers or a one-off prompt rewrite. I inspect the pipeline behind the output: inputs, prompts, context assembly, retrieval/reranking, model behavior, SFT or fine-tuning data if relevant, output schema, post-processing, business rules, and feedback/evaluation signals.
You can use this for RAG, chatbots, document QA, AI-generated copy, recommendation explanations, structured LLM outputs, or other AI product features.
Depending on the package, you will receive a lightweight snapshot, structured failure analysis, evaluation cases, scoring rubric, and/or an evaluation workflow blueprint. The goal is to turn vague quality complaints into clear failure types, root causes, and prioritized fixes you can act on.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, PyTorchAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$99
|
Standard
$499
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 10 days |
Number of Revisions | 0 | 1 | 1 |
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 | - | - | - |
Frequently asked questions
About Quinn
Senior AI/ML Engineer | 10+ Yrs | Ranking, Growth & LLM Reliability
Tokyo, Japan - 9:41 pm local time
I’m a Senior AI/ML Engineer with 10+ years of experience building production machine learning, recommendation, user growth, and backend systems at leading internet technology companies. My work has supported large-scale consumer platforms, including global short-video and content ecosystems with hundreds of millions of daily active users.
What I can help with:
1. Product Algorithms & ML Systems
- Recommendation, ranking, matching, personalization, and search relevance
- Offline/online evaluation, model diagnosis, and A/B experiment design
- Predictive modeling, forecasting, classification, and decision systems
- Content intelligence, semantic analysis, and NLP pipelines
2. Growth Intelligence & Data-Driven SEO
- Query opportunity modeling and search traffic growth
- Programmatic SEO strategy for large-scale content/page systems
- Cohort analysis, uplift modeling, incentive allocation, and ROI optimization
- Data-driven growth experiments and performance measurement
3. Reliable LLM / RAG Systems
- RAG quality audits, retrieval improvement, and reranking strategy
- Hallucination reduction, source grounding, and citation-backed answers
- LLM evaluation, failure taxonomy, structured outputs, and test cases
- Production-readiness reviews for AI features and agentic workflows
I work best on problems where model quality, business metrics, and production constraints all matter: ranking quality, growth ROI, retrieval accuracy, evaluation design, latency, reliability, and maintainability.
If you need someone who can connect AI/ML capabilities with real product metrics, data pipelines, and production engineering, I can help diagnose, design, and ship a solution that works in practice.
Steps for completing your project
After purchasing the project, send requirements so Quinn can start the project.
Delivery time starts when Quinn receives requirements from you.
Quinn works on your project following the steps below.
Revisions may occur after the delivery date.
Review product goal and pipeline
I review your AI feature goal, pipeline structure, model setup, prompts, context flow, output format, and success criteria.
Analyze positive and negative cases
I inspect the provided sample cases, compare good and bad outputs, and identify recurring quality and reliability patterns.