Analyzing User Interactions with LLMs to Improve our Documentation

Enhancing documentation through AI-enabled tools like Mendable reveals challenges in summarizing large datasets of user questions to identify documentation gaps. Two methods were explored: clustering similar questions for summarization and a map-reduce approach using LangChain to split, summarize, and synthesize questions. Each method has trade-offs; while map-reduce offers high customizability and detailed thematic breakdowns, it incurs higher costs due to token usage. Clustering, though riskier for information loss, provides a cost-effective option for compressing large datasets before detailed summarization. Testing these methods with LangChain and GPT models, the study found a balance between cost and information fidelity, suggesting a combined approach could effectively enhance documentation insights.

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