I will build a generative ai chatbot using openai, gemini, or langchain


About this gig
I build custom generative AI chatbots and applications that answer questions about your business, documents, or product not generic responses.
Relevant experience: During my AI internship with IBM SkillsBuild/Edunet Foundation, I built an NLP-based mental health tracker with sentiment analysis, mood prediction, and chatbot-style conversational assistance, combining machine learning with responsible AI practices.
What I can build for you:
- Custom chatbots powered by OpenAI GPT or Google Gemini
- RAG (Retrieval-Augmented Generation) chatbots that answer from your documents
- AI agents for task automation
- Vector database setup (FAISS, ChromaDB)
- Prompt engineering for consistent, on-brand responses
- LoRA fine-tuning for specialized use cases
- Integration into your website, app, or internal tool via API
Tech: LangChain, Hugging Face, OpenAI/Gemini APIs, FAISS, ChromaDB, FastAPI, Python
Why work with me: I understand both ML fundamentals and modern LLM tooling (RAG, vector DBs, agents), backed by AI internships from IBM and Microsoft/SAP.
Message me with your use case before ordering so I can scope it accurately.
Get to know Sudhanshu
Artificial Intelligence, Machine Learning , MERN Stack Developer
- FromIndia
- Member sinceMar 2024
- Avg. response time1 hour
Languages
Hindi, English
FAQ
What's the difference between a simple chatbot and a RAG chatbot?
A simple chatbot answers from general AI knowledge. A RAG chatbot answers from your specific documents or data — accurate to your business, not generic.
Can the chatbot work with my existing website or app?
Yes — I deliver it as an API you can integrate, or I can help wire it directly into your platform (Premium tier).
What kind of documents can it read from?
PDFs, text files, websites, or structured data — send me a sample and I'll confirm compatibility.
Do you use OpenAI or open-source models?
Both, depending on your budget and privacy needs — OpenAI/Gemini for speed, or open-source models via Hugging Face for more control.
