I build RAG (Retrieval-Augmented Generation) chatbots — AI assistants that answer from your own documents, product data, or knowledge base instead of guessing. Using OpenAI/LLM APIs, vector databases, and custom retrieval pipelines on Node.js and Python, with React/Next.js chat interfaces.
What I Build
- Custom RAG chatbots grounded in your documents and knowledge base
- Internal AI assistants for team knowledge search
- Customer-facing support chatbots with on-brand guardrails
- AI search for websites and product catalogs
- Streaming chat UIs built with React and Next.js
Real Use Cases
- AI Agents & Automation — OpenAI/LLM integrations, chatbots, workflow automation
- AI agent integrations for full-stack, hourly projects
- Backend & APIs — Go, Node.js, NestJS, Python, GraphQL, REST
- Full-Stack Web — React, Next.js, and database-backed apps
Technology
- OpenAI and other LLM APIs
- Vector databases for retrieval (Pinecone, pgvector, and similar)
- Node.js and Python backends
- React and Next.js chat interfaces with streaming responses
Built to Answer From Your Data, Not Guess
A chatbot that hallucinates answers erodes trust fast. I build retrieval pipelines that ground every response in your actual documents, with guardrails and system prompts that keep the bot on-brand and prevent it from answering outside its scope.
FAQ
What’s the difference between RAG and fine-tuning?
RAG retrieves relevant chunks of your data at query time and feeds them to the LLM as context, so the model always answers from up-to-date information. Fine-tuning bakes knowledge into the model itself and is slower to update. For most chatbot use cases, RAG is the faster, cheaper, and more maintainable option — I’ll help you decide which fits your case.
What data can the chatbot use?
Documents, PDFs, help center articles, product catalogs, internal wikis, or a database — anything that can be chunked and embedded into a vector database. The chatbot retrieves the most relevant pieces for each question before generating an answer.
How do you prevent the bot from giving wrong or off-brand answers?
Through system prompts, guardrails, and scoping the retrieval so the bot only draws from approved sources. I also test output quality before launch rather than shipping on trust alone.
Related Reading
- Prompt Engineering vs Fine-Tuning vs RAG: A Decision Framework
- Vector Databases Explained: Why Your AI App Needs One
- How to Keep an AI Chatbot On-Brand: Guardrails and System Prompts Explained
- Evaluating AI Output Quality: How to Actually Test a Chatbot Before Launch
- Building an Internal AI Assistant: Automating Knowledge Search for Your Own Team
Discuss Your Project
Have a RAG chatbot or AI assistant idea? Get in touch to talk through scope, or reach out via Fiverr if you’d rather start there.