This podcast explores why testing an LLM chatbot in an MCP-based system requires a different QA mindset than testing traditional deterministic software. It explains how MCP orchestration, RAG pipelines, tool calls, WebSockets, and streaming responses create multiple layers where failures can occur. The article also shows how a custom Python-based test framework can validate chatbot output through must-have checks, must-not rules, and semantic similarity analysis. Special attention is given to hallucination prevention, configuration-dependent results, and the challenges of testing multi-turn conversations. For teams building AI-powered products, it offers a practical look at how structured QA can make LLM systems more reliable, measurable, and business-safe.

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