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English
$
USD
I will fix and optimize your rag chatbot accuracy
Amine E.
About this gig
Does your RAG chatbot retrieve wrong chunks, miss exact IDs, or answer confidently from weak evidence?
I trace the path from question to retrieval, context, answer, and citation to find where the failure starts.
You can receive:
- Tests using your failing questions
- Retrieval, grounding, and citation diagnosis
- Scoped code or configuration fixes
- Before-and-after results
- Handoff notes and prioritized next steps
I investigate chunking, metadata filters, exact IDs, hybrid retrieval, citations, prompt grounding, and insufficient-evidence behavior.
I built a manufacturing RAG assistant with hybrid semantic/full-text retrieval, exact-ID handling, streamed answers, and page citations. I evaluated it on an internal 58-case, 99-turn benchmark covering facts, grounding, citations, safety, and multi-turn behavior.
Primary stack: Node.js, TypeScript, LangChain, OpenAI, and PostgreSQL/pgvector.
Message me before ordering with your stack and three failing examples. Never send
passwords, API keys, or private credentials.
Get to know Amine E.
Amine E.
RAG and Full Stack AI Developer
- FromMorocco
- Member sinceAug 2026
Languages
Arabic, English, French
I build source-grounded AI chatbots that answer from PDFs, manuals, procedures, and knowledge bases with verifiable citations. I work with document ingestion, chunking, embeddings, vector and full-text retrieval, OpenAI, LangChain, TypeScript APIs, React interfaces, authentication, evaluation, and deployment.
My flagship project is a manufacturing service assistant with PostgreSQL/pgvector, exact-identifier search, streamed answers, page-level sources, multi-turn troubleshooting, administration, and pilot monitoring. I choose the smallest reliable architecture for each workflow.
My Portfolio
FAQ
Do I need an existing RAG chatbot?
Yes. This Gig is primarily for diagnosing and improving an existing RAG chatbot or retrieval pipeline. If you need a new RAG assistant built from scratch, please use one of my RAG development Gigs.
Can you guarantee zero hallucinations?
No responsible RAG system can guarantee that an LLM will never produce an incorrect response. I focus on improving retrieval, grounding, citations and insufficient-evidence behavior, and on measuring the system with test questions.
What RAG problems can you investigate?
Examples include irrelevant retrieval, missing documents, weak chunking, metadata filters, exact identifiers not being found, poor context construction, citation problems and unsupported answers.
What technologies do you work with?
My main stack is Node.js/TypeScript, LangChain, PostgreSQL/pgvector and OpenAI. I can also review custom RAG architectures where the code and components are accessible.
What do you need from me?
I need a description of the problem, your current architecture or repository, sample documents/data if required, and examples of questions where the system performs incorrectly.
Will you measure improvement?
Standard and Premium can include an evaluation set and before/after testing so improvements are based on actual test cases rather than subjective impressions.
Can you implement hybrid search?
Yes, when it is appropriate for the problem. This may combine semantic retrieval with keyword or full-text retrieval, particularly when users need both natural-language search and exact identifiers.
