Yasir N.

I have a strange habit when a business tells me their AI kind of works, I can't let it go until I know why it doesn't. Because here's what I've learned building LLM and RAG systems for the last few years an AI that makes things up isn't an AI problem. It's a systems problem. The model was never the weak link. The retrieval was. The data was. The architecture nobody designed on purpose was. That's the part I love. Not the demo that dazzles for five minutes, but the system underneath that still gives the right, sourced answer on the ten-thousandth question. I'm an LLM and RAG engineer, and I think like a strategist first: what does this answer need to be true, and how do I prove it? Here's how I see it. Most teams bolt a chatbot onto a knowledge base and hope. I do the unglamorous work that actually matters chunking strategy, embeddings, hybrid retrieval vector + keyword, reranking, evaluation so the right context reaches the large language model before it ever speaks. That's what kills hallucination. That's what makes an answer something you can put in front of a customer or a regulator. What that looks like in practice: I build custom RAG systems and AI knowledge bases that turn your documents, wikis, and databases into something your team and customers can actually query with citations, so every answer is traceable. I build LLM-powered chatbots and conversational AI with real memory and multi turn context. And I integrate large language models into existing apps through clean, scalable APIs, with the retrieval, prompt engineering, and evaluation pipelines that keep them accurate in production. My toolkit: GPT-4o, Claude, Gemini, Mistral, LLaMA · Pinecone, Weaviate, ChromaDB, Qdrant · LangChain, LlamaIndex · Python, FastAPI · embeddings, semantic search, vector databases, RAG evaluation. I've done this across SaaS, healthcare, legal, real estate, e-commerce, and education anywhere being almost right isn't good enough. So tell me what your AI needs to know your docs, a database, a messy help center and I'll tell you exactly how I'd make it accurate, and how I'd prove it works. That last part matters more than people think.