LLM Customer Support Chatbot
Budget: $15 – $25 USD
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.
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.