I will clean, prepare and structure your data for llm training or rag pipelines

Is your business data ready for AI, or will it break your LLM/RAG system?

Most AI projects fail not because of the model, but because of messy, unstructured input data. I clean, chunk, and structure your business data so it's actually ready for LLM training, fine-tuning, or RAG (Retrieval-Augmented Generation) pipelines.

What I can do for you:

• Convert raw text, CSVs, or documents into clean JSON/JSONL format
• Chunk and tokenize large files (PDFs, text, databases) for vector database injection
• Prepare data ready to inject into Pinecone, Chroma, LangChain, or LlamaIndex
• Build automated Python scripts so new incoming data gets cleaned and structured continuously

Why this matters: Feeding an LLM messy, inconsistent data leads to hallucinations and poor retrieval accuracy. I write custom Python scripts (Pandas, tokenization logic) to fix that before it ever reaches your model or vector store.

I built this exact kind of data-cleaning-to-LLM pipeline for a compliance-scanning tool that processes real business documents and feeds them into an LLM for structured, rule-checked output.

Tech stack: Python, Pandas, LangChain, Llam