You will get a RAG audit for hallucinations, retrieval quality, and access control
Project details
Your RAG app can cite sources and still fail in three different ways: it may retrieve the wrong evidence, generate claims the evidence does not support, or expose content to the wrong role.
I will audit an existing RAG pipeline—not rebuild it. You receive a scoped test set, reproducible findings, and a prioritized remediation roadmap. Each finding records the query, test role, expected behavior, retrieved evidence, observed answer, severity, and recommended fix.
Depending on the tier, I test retrieval relevance, answer grounding and citations, stale or conflicting content, role or tenant boundaries, and limited non-destructive prompt-injection or poisoned-document scenarios.
This is a diagnostic engagement. It is not a penetration test, compliance certification, model fine-tuning project, production deployment, or promise of a hallucination-free system. Production changes are not included.
I will audit an existing RAG pipeline—not rebuild it. You receive a scoped test set, reproducible findings, and a prioritized remediation roadmap. Each finding records the query, test role, expected behavior, retrieved evidence, observed answer, severity, and recommended fix.
Depending on the tier, I test retrieval relevance, answer grounding and citations, stale or conflicting content, role or tenant boundaries, and limited non-destructive prompt-injection or poisoned-document scenarios.
This is a diagnostic engagement. It is not a penetration test, compliance certification, model fine-tuning project, production deployment, or promise of a hallucination-free system. Production changes are not included.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
Conversational AI, Natural Language UnderstandingAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$99
|
Standard
$349
|
Advanced
$799
|
|---|---|---|---|
| Delivery Time | 2 days | 4 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 | - | - | - |
Frequently asked questions
About Lielie
AI App & Systems Integration Engineer | APIs, Automation, DevOps
Changzhou, China - 9:23 am local time
If you need more than a chatbot demo, I can turn a repetitive business process into a working web application, automation, AI agent, or internal tool. I handle the full delivery process—from requirements and architecture to APIs, databases, deployment, testing, and ongoing support.
With 15+ years of software delivery experience, I have built and maintained enterprise and public-sector systems that require reliability, security, and long-term operation.
I can help you with:
- AI agents and workflow automation
- AI integration for existing web applications
- Internal tools, dashboards, and admin portals
- API, webhook, database, and third-party integrations
- Document processing, reporting, search, and knowledge systems
- Legacy system modernization, debugging, and on-premises deployment
My approach is practical: define the workflow and acceptance criteria, build a testable milestone, verify edge cases, and then make the system stable enough for daily use.
Send me your current workflow, files, or system requirements. I can help you identify the simplest reliable solution before development begins.
Steps for completing your project
After purchasing the project, send requirements so Lielie can start the project.
Delivery time starts when Lielie receives requirements from you.
Lielie works on your project following the steps below.
Revisions may occur after the delivery date.
Scope and evidence freeze
I confirm the environment, sources, roles, test limits, success criteria, and excluded work. Missing or unsafe access is resolved before the delivery clock starts.
Evaluation set calibration
I normalize the supplied questions and expected evidence, add agreed edge cases, and create traceable test IDs. Domain truth and permission expectations remain client-approved.