I will build your chatbot as rag pipeline for any application

If your AI system is hallucinating, retrieving irrelevant context, or just not performing on your proprietary data I can fix it. If you're starting from scratch, I'll build it right the first time.

What you get:

A production-grade Retrieval-Augmented Generation pipeline that grounds your LLM in your actual data with proper chunking strategies, embedding optimization, retrieval evaluation, and hallucination guardrails.

What I solve:

• RAG systems returning irrelevant or incorrect information
• Poor retrieval accuracy on domain-specific or technical documents
• Chunking strategies that destroy context in complex documents (tables, cross-references, multi-language content)
• No evaluation framework you don't know if your RAG is actually working
• Scaling a working prototype to production

Deliverables:

• Document ingestion and preprocessing pipeline
• Vector database setup and optimization (Pinecone, Weaviate, ChromaDB, Qdrant)
• Retrieval pipeline with reranking and hybrid search where appropriate
• Evaluation framework with quantified retrieval and generation metrics
• Documentation and deployment guide

Tech: LangChain, LlamaIndex, OpenAI Embeddings, Cohere Rerank, Pinecone, Weaviate, ChromaDB, Python,