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I will test your rag chatbot for hallucinations
Iftakhar Niazi
Level 1
About this gig
Does your RAG chatbot sound confident while using the wrong document, missing important context, or inventing unsupported answers?
I will evaluate your retrieval augmented generation system with a structured test set and a clear quality report not a few casual prompts.
Depending on your package, the evaluation can include:
- representative and adversarial questions
- versioned golden test cases
- context precision and context recall
- faithfulness or groundedness review
- answer relevance and correctness
- citation and source-support checks
- retrieval and generation failure categories
- comparison of two agreed system versions
- repeatable evaluation script and regression baseline
You receive the dataset, scores, failure examples, limitations, and prioritized recommendations for retrieval, chunking, prompts, or answer behavior.
This service measures risk; it does not guarantee zero hallucinations. Results depend on the documents, reference answers, judge model, sampling settings, and human review available.
Please message me before ordering if your data is confidential, regulated, multilingual, highly technical, or larger than the package scope.
Get to know Iftakhar Niazi
Iftakhar Niazi
AI powered automation for seamless efficiency
Level 1
- FromPakistan
- Member sinceMay 2024
- Avg. response time1 hour
- Last delivery1 month
Languages
Spanish, French, Arabic, German, English
Hi there, I’m Iftakhar – an AI developer and UI/UX designer with 3+ years of experience in building AI-driven solutions, seamless web applications, and intuitive user experiences. With 90+ projects completed, I specialize in AI bot development, app development, and advanced web scraping. My expertise spans NLP, deep learning, and generative AI, alongside UI/UX tools like Figma, Adobe XD, and Sketch. With Python, Java, C++, and Selenium, I create intelligent, user-friendly, and visually engaging digital solutions.
My Portfolio
FAQ
Can you guarantee my chatbot will stop hallucinating?
No. Evaluation identifies and measures failure patterns. It cannot prove that an open-ended language model will never produce an unsupported response.
Which frameworks can you use?
RAGAS or a lightweight custom evaluation harness can be used depending on the stack and metrics. The exact judge model and versions will be documented.
Do I need reference answers?
They improve correctness evaluation. If none exist, Basic can help draft a candidate set from approved public or buyer-provided documents, but domain validation remains the buyer's responsibility.
Can you evaluate LangChain, LlamaIndex, or a custom API?
Yes, if the system exposes a safe repeatable interface and the buyer provides setup or API instructions.
Is model/API usage included?
Small agreed usage may be included only when stated. Larger API, vector database, hosted model, or evaluation-platform costs are paid by the buyer.
How do you protect private data?
Use minimized, redacted, non-production data where possible. Any third-party model use must be approved. Secrets are stored outside source code and removed from deliverables.
Can you compare two RAG versions?
Yes. Premium includes up to two agreed versions, such as different retrievers, chunking strategies, prompts, or models.
What is not included?
Building the complete chatbot, training a new model, domain certification, red-team security testing, and unlimited document labeling are separate services.
