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$
USD
I will build a production ready rag chatbot with langchain and python
Fahad Shah
About this gig
Most "RAG chatbots" are just a PDF stuffed into a vector store with no way to verify the answers are correct.
I build RAG systems the way they're built in production and I test them. Every project includes an evaluation pipeline so you know your chatbot's accuracy isn't a guess.
What I Build:
- Custom RAG chatbots trained on your documents, PDFs, or knowledge base
- Hybrid search + reranking for real retrieval quality, not just basic similarity search
- LLM integration: GPT-4o, Claude, LLaMA, open-source models
- Accuracy testing so you can trust the output before it reaches users
- Full-stack delivery: backend, API, and a working frontend
Tech Stack:
Python, LangChain, Hybrid Search, CrossEncoder Reranking, FAISS/Pinecone, LLaMA 3.1, OpenAI, Streamlit/React
Why Me:
I currently work on an AI product team building an ad-performance platform, and I built a production RAG system over real research data with hybrid search, reranking, and a full evaluation framework not a demo.
Message me before ordering with your use case and I'll scope it properly first.
Get to know Fahad Shah
Fahad Shah
AI and Web Developer RAG NLP and Modern Web Apps
- FromPakistan
- Member sinceAug 2023
Languages
Urdu, English
I am a Computer Scientist with hands-on experience in AI/NLP engineering, RAG, and reinforcement learning. I have a demonstrated ability to design production-grade AI systems, including a medical RAG pipeline. I possess a strong foundation in Python, C/C++, and ML frameworks, with a focus on building grounded, evaluable LLM systems. Furthermore I have hands on experience in building websites and landing pages with strong foundation in html, css and react.
My Portfolio
FAQ
Q1: Will the AI chatbot hallucinate or make up facts?
No. RAG forces the AI to answer only from the data you provide. If the answer isn't in your documents, the bot says so instead of guessing — and I test this before delivery with an evaluation pipeline.
Q2: How is this different from a basic chatbot wrapper?
A wrapper just calls an API with a prompt. RAG retrieves from your actual data first, then generates an answer grounded in it — so responses are accurate, not generic.
Q3: Do I own the source code?
Yes, fully — code and documentation are yours to keep (included from the Standard package upward).
Q4: What if I have a custom use case not listed here?
Message me your requirements before ordering and I'll scope a solution specifically for your data and use case.
