I need a Large Language Model solution that can hold realistic, two-way conversations with users who are seeking technical assistance. The focus is customer support, specifically guiding people through troubleshooting steps, clarifying error messages, and recommending next actions when a fix fails. Unlike a simple FAQ bot, the assistant should ask follow-up questions, reference product documentation or ticket history, and adapt its language to a user’s skill level. Here’s how I see the engagement: • Design and fine-tune the conversational flow so the model feels like a seasoned support agent—empathetic, concise, and technically precise. • Implement retrieval-augmented generation (e.g., LangChain + vector store) so the bot can pull the latest knowledge-base articles, log snippets, or release notes at runtime. • Build a lightweight API (Python/FastAPI preferred) that my front-end team can call with user messages and receive formatted responses plus confidence scores. • Include guardrails for escalation: if the model’s confidence drops below a threshold or it detects a safety issue, it should transfer the session to a human queue with the chat transcript attached. • Provide a short README with setup instructions, environment variables, and one-click deployment to our existing Docker/Kubernetes stack. Acceptance criteria 1. A demo showing the assistant resolving at least three different technical issues end-to-end without hallucinations. 2. Accuracy above 90 % on a curated set of troubleshooting prompts we will supply. 3. Source code, fine-tuning scripts, and all model assets delivered through a private Git repository. If you have previous experience integrating OpenAI, Anthropic, or open-source LLMs (Llama-2, Mistral) into customer-support workflows, that will help us move faster, but I’m open to the best stack for the job.