RAG & Vector Search Systems
I'm Muhammad Hamd, an AI engineer from Karachi, Pakistan, and I build retrieval-augmented generation (RAG) and vector search systems for technical teams worldwide. If your AI needs to answer from your documents, knowledge base, or product data accurately and with sources, then you need real retrieval rather than a bigger prompt. I design RAG pipelines end to end, which covers chunking, embeddings, vector storage, retrieval tuning, and evaluation, using Pinecone, FAISS, Weaviate, or pgvector.
What this solves
- ✓AI that hallucinates because it isn't grounded in your real data
- ✓Search that returns irrelevant chunks and poor answers
- ✓Large, changing knowledge bases the model can't fit in a prompt
- ✓Needing answers with citations and traceability for trust
What I build
Ingestion & chunking
Pipelines that parse and chunk your documents and data intelligently, so retrieval returns the right context instead of noise.
Embeddings & vector storage
The right embedding model and vector store for your scale and budget, whether Pinecone, Weaviate, FAISS, or pgvector, set up for fast and accurate search.
Retrieval tuning
Hybrid search, re-ranking, and metadata filtering so the most relevant context reaches the model every time.
Evaluation & citations
Retrieval evaluation together with grounded, cited answers, so you can measure quality and trust the output.
Tools & stack
Keep exploring
Frequently asked
What is a RAG system?+
Retrieval-Augmented Generation retrieves relevant pieces of your own data and feeds them to an LLM at query time, so answers are grounded in your information instead of the model's training data, which keeps them accurate, current, and citable.
Which vector database should I use?+
It depends on scale, budget, and infrastructure. pgvector is great if you already run Postgres, Pinecone is a managed option, and Weaviate or FAISS suit other needs. I will recommend one based on your requirements rather than a default.
How do you improve RAG accuracy?+
Through better chunking, hybrid search that combines keywords and vectors, re-ranking, metadata filtering, and retrieval evaluation. Most weak RAG systems fail at retrieval rather than at the LLM, and that is where I focus.
Can you add RAG to my existing AI app?+
Yes. I can add a retrieval layer to an existing LLM application so it answers from your data, with evaluation to prove the quality improvement.
Want rag & vector search for your team?
Tell me what you're trying to build. I'll reply with whether I can help and how I'd approach it.