Real-Time Hybrid RAG Assistant Developer (AI / LLM)
Developed and fine-tuned a Hybrid Retrieval-Augmented Generation (RAG) assistant for telecom query resolution. The project focused on training large language models using retrieval-based context and prompt engineering strategies. Assisted in the improvement of AI-generated responses by adjusting context-scoring and integrating live knowledge base updates. • Built conditional routing agents in LangFlow to solve telecom domain queries. • Implemented architecture combining static knowledge with live API data sources. • Employed strategies to enhance real-time, contextually relevant LLM output. • Supported proactive network monitoring based on AI-generated insights.