In the rapidly evolving domain of software engineering (SE), Large Language Models (LLMs) are increasingly leveraged to automate developer support. Open source LLMs have grown competitive with proprietary models such as GPT-4 and Claude-3, without the associated financial and accessibility constraints. This study investigates whether state of the art open source LLMs including Solar-10.7B, CodeLlama-7B, Mistral-7B, Qwen2-7B, StarCoder2-7B, and LLaMA3-8B can generate responses to technical queries that align with those crafted by human experts. Leveraging retrieval augmented generation (RAG) and targeted fine tuning, we evaluate these models across critical performance dimensions, such as semantic alignment and contextual fluency. Our results show that Solar-10.7B, particularly when paired with RAG and fine tuning, most closely replicates expert level responses, offering a scalable and cost effective alternative to commercial models. This vision paper highlights the potential of open-source LLMs to enable robust and accessible AI-powered developer assistance in software engineering.