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$
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I will build and deploy a production rag system
AYESHA SHAHID
About this gig
Are you tired of searching through long PDFs or reports? I will build a custom RAG (Retrieval-Augmented Generation) application to chat with your documents and get accurate, cited answers instantly.
I am a published ML researcher (Springer Nature, 2026) with hands-on experience building production RAG systems, not just basic tutorials.
WHAT I BUILD:
- PDF/document Q&A system: upload any document, ask questions, get cited answers.
- Knowledge base chatbot connects your data, policies, or manuals to an AI assistant.
- Medical/legal/technical document assistant.
- RAG pipeline with hallucination prevention and source citations.
- Multi-document RAG with filtering and metadata search
MY TECH STACK:
- LangChain + FAISS / ChromaDB for retrieval
- Llama 3, Mistral, or GPT-4 for generation
- Streamlit or FastAPI for the interface
- Docker for deployment
- HuggingFace Spaces or your preferred cloud
WHY CHOOSE ME:
- Deployed real RAG systems with live demos available.
- Academic rigor: I build AI that is accurate, not just impressive.
- Deliveries include clean source code and documentation.
Message me before ordering to discuss your data structure. Let's build your AI assistant.
Get to know AYESHA SHAHID
AYESHA SHAHID
ML Researcher, Healthcare AI, RAG and LLM Apps, Springer Published
- FromPakistan
- Member sinceMay 2026
- Avg. response time1 hour
Languages
Urdu, English
Published ML researcher (Springer Nature, 2026) specializing in healthcare AI, RAG applications, and production ML systems.
I've trained 15+ model architectures, including BiLSTM, CNN, XGBoost, and LLMs across medical and NLP domains. I build RAG apps with LangChain and FAISS, ML pipelines with FastAPI and Docker, and deep learning models for classification and prediction.
Every delivery is clean, documented, and production-ready, not just a notebook.
FAQ
What specific frameworks and vector databases do you use to build the RAG system?
I primarily build native Python pipelines utilizing LangChain for orchestration, FAISS or ChromaDB for high-performance vector storage, and HuggingFace for custom text embeddings. For the user interface, I use Streamlit (Basic package) or build robust backend APIs using FastAPI (Standard/Premium).
How do I know the RAG pipeline will be accurate and handle complex data?
I am a published Machine Learning researcher (Springer Nature, 2026). Unlike generic wrappers, I handle data chunk optimization, overlap tuning, and custom system prompt engineering natively. This ensures your system mitigates hallucinations and returns accurate, cited references from data source.
What types of documents can the chatbot parse and read?
Out of the box, the system supports structured and unstructured PDFs, TXT files, and markdown knowledge bases. If your documents contain heavy multi-column corporate reports or specific tables, please message me first so we can discuss the preprocessing script requirements.
Who covers the LLM API costs, and is my data secure?
The buyer provides the API keys (such as OpenAI, Anthropic, or Groq) which are securely injected using environment variables (.env). Your document data stays completely private within your local vector index or preferred cloud deployment environment.
