LLMs hallucinate when they don't know the answer.
RAG systems fix that by making the AI read your data first.

Retrieval Augmented Generation means the AI searches through your 
actual documents, PDFs, databases, or knowledge base before it 
responds. The result is answers that are accurate, cited, and 
grounded in your real information — not guesses.

I have built RAG pipelines that process PDF documents, chunk and 
embed them into vector databases, retrieve the most relevant context, 
and pass it to an LLM to generate a precise response.

What this looks like in practice:

Upload your company knowledge base. Ask it anything. Get accurate 
answers with source references — not hallucinated responses.

What the build includes:
Document ingestion and preprocessing.
Chunking strategy and embedding generation.
Vector database setup — Pinecone, FAISS, or ChromaDB.
Retrieval logic with relevance ranking.
LLM response generation via OpenAI or Gemini API.
API layer built in FastAPI for integration into your product.

If your business has documents, manuals, FAQs, or data that 
customers or staff need to query — a RAG system makes that 
instant and accurate.