Auto-Eval of Question-Answering Tasks

Lance Martin introduces an evaluative tool for Question-Answering (QA) chains using LLM ops platforms, like LangChain, which assemble components such as models and document retrievers into chains for applications like QA. The tool, implemented as a Streamlit app, allows users to input documents and, optionally, corresponding question-answer pairs; it can also auto-generate these pairs using a QAGenerationChain. Users can customize QA chains by selecting different document retrievers, split methods, and LLMs for summarizing answers. The tool uses GPT-3.5-turbo to score the quality of retrieved documents and answers, with results presented for human inspection, enabling users to engineer prompts and compare performance across configurations. Future enhancements aim to include more retrievers and models, improve latency, offer a free hosted tool, and extend capabilities to other tasks like chat, with automated chain assembly based on user objectives.

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