Hi, I came across your post and even though some time has passed, I wanted to reach out because your project aligns exactly with the kind of RAG-based AI work I specialize in. If you're still exploring how to implement or refine Retrieval-Augmented Generation within your AI system, I’d be excited to step in with a clear, efficient, and scalable solution. 🚀 How I Can Help You Right Away I will work hands-on to: Design and implement a robust RAG pipeline tailored to your system Integrate with vector databases like FAISS, Pinecone, or Weaviate for high-speed retrieval Optimize your embedding models to ensure better context understanding Fine-tune LLMs (OpenAI, GPT, Llama, Claude, etc.) for task-specific accuracy Improve document indexing, retrieval speed, and overall response quality Seamlessly integrate into your existing AI framework or tech stack Perform testing, evaluation, and iteration for performance improvement Tools & Technologies I Use Languages & Frameworks: Python, LangChain, Transformers Vector DBs: FAISS, Pinecone, Weaviate, ChromaDB LLMs: GPT (3.5/4), LLaMA, Claude, Hugging Face Other: OpenAI API, Streamlit, Flask, cloud integrations Why Work with Me? I’ve worked with startups and tech teams to embed RAG into SaaS, internal knowledge bots, and customer-facing assistants — producing faster, smarter responses and freeing teams from repeated manual tasks. Even if you’ve paused or assigned the project, I’d love to offer a free audit or roadmap suggestion, just to help you get clarity or improve what's already built. Thanks for considering my proposal and if you're ready to move forward, I’m ready to jump in.