LLM QA Reviewer / AI Output Validator (RAG Systems & AI Reliability)
Budget: £750 – £1,500 GBP
Description
We are building a network of specialists who help validate and improve the reliability of enterprise AI systems.
Our company, ProoflineAI.com , works with organisations deploying AI assistants and knowledge systems powered by large language models and RAG (Retrieval-Augmented Generation).
Your role will be to review AI responses, detect hallucinations, validate grounding against source material, and help create evaluation datasets.
This is not model development.
This role focuses on AI quality assurance and validation.
Responsibilities
• Review LLM responses for factual accuracy
• Identify hallucinations and fabricated references
• Verify whether answers are grounded in provided documents
• Detect retrieval vs generation failures in RAG systems
• Score responses using structured evaluation guidelines
• Create test prompts and edge-case scenarios
• Document failure patterns clearly
Ideal Candidate
You may be a strong fit if you have experience in:
• AI QA or AI annotation
• NLP systems or LLM evaluation
• data quality or QA testing
• ML Ops or AI operations
• reviewing AI-generated content critically
Strong written English and attention to detail are essential.
Location
Preferred regions:
Poland
Romania
Portugal
Estonia
Latvia
Lithuania
Compensation
Typical range depending on workload:
€800 – €1,200 per month (part-time)
€1,200 – €1,800 per month (full-time)
Long-term collaboration possible.
Important Application Instruction
To confirm you read the description, start your proposal with the word: RELIABILITY.
Then answer the questions below.
Applications without answers will not be considered.
2️⃣ 5-Minute AI Test (Filters ~95% of Candidates)
Send this immediately after application.
Question 1 — Hallucination Detection
AI response:
"According to the 2021 Brown vs Carter ruling, all digital employment contracts must include biometric authentication."
You cannot find this case in legal databases.
What is the most likely issue?
A) Retrieval failure
B) Hallucination / fabricated reference
C) Prompt formatting issue
D) Translation error
Correct answer: B
Question 2 — RAG Understanding
In a RAG-based AI system, what is the difference between:
• retrieval failure
• generation failure
Answer in 2–3 sentences.
Good candidates explain:
Retrieval failure = wrong documents retrieved
Generation failure = model misinterprets correct context
Question 3 — QA Thinking
You receive an AI response that cites a document.
What are the first 2 steps you take to verify if the answer is correct?
Expected answers include:
• checking the source document
• verifying grounding
• comparing answer with retrieved context
We are building a network of specialists who help validate and improve the reliability of enterprise AI systems.
Our company, ProoflineAI.com , works with organisations deploying AI assistants and knowledge systems powered by large language models and RAG (Retrieval-Augmented Generation).
Your role will be to review AI responses, detect hallucinations, validate grounding against source material, and help create evaluation datasets.
This is not model development.
This role focuses on AI quality assurance and validation.
Responsibilities
• Review LLM responses for factual accuracy
• Identify hallucinations and fabricated references
• Verify whether answers are grounded in provided documents
• Detect retrieval vs generation failures in RAG systems
• Score responses using structured evaluation guidelines
• Create test prompts and edge-case scenarios
• Document failure patterns clearly
Ideal Candidate
You may be a strong fit if you have experience in:
• AI QA or AI annotation
• NLP systems or LLM evaluation
• data quality or QA testing
• ML Ops or AI operations
• reviewing AI-generated content critically
Strong written English and attention to detail are essential.
Location
Preferred regions:
Poland
Romania
Portugal
Estonia
Latvia
Lithuania
Compensation
Typical range depending on workload:
€800 – €1,200 per month (part-time)
€1,200 – €1,800 per month (full-time)
Long-term collaboration possible.
Important Application Instruction
To confirm you read the description, start your proposal with the word: RELIABILITY.
Then answer the questions below.
Applications without answers will not be considered.
2️⃣ 5-Minute AI Test (Filters ~95% of Candidates)
Send this immediately after application.
Question 1 — Hallucination Detection
AI response:
"According to the 2021 Brown vs Carter ruling, all digital employment contracts must include biometric authentication."
You cannot find this case in legal databases.
What is the most likely issue?
A) Retrieval failure
B) Hallucination / fabricated reference
C) Prompt formatting issue
D) Translation error
Correct answer: B
Question 2 — RAG Understanding
In a RAG-based AI system, what is the difference between:
• retrieval failure
• generation failure
Answer in 2–3 sentences.
Good candidates explain:
Retrieval failure = wrong documents retrieved
Generation failure = model misinterprets correct context
Question 3 — QA Thinking
You receive an AI response that cites a document.
What are the first 2 steps you take to verify if the answer is correct?
Expected answers include:
• checking the source document
• verifying grounding
• comparing answer with retrieved context