How STORM Uses LLMs to Automate Technical Research and Writing

1. Introduction

In daily development, to ensure the quality of technical solutions, researchers often conduct preliminary investigations. When lacking prior knowledge, the author usually searches keywords, which can be biased by keyword abstraction and search‑engine ranking. With the rise of large models, the author sought an AI tool that can automatically retrieve and summarize information, discovering STORM.

2. What is STORM?

STORM is a knowledge‑management system developed at Stanford University based on large language models (LLM). It conducts research on a specific topic and generates a complete report with citations. The system consists of a pre‑writing stage and a writing stage: it gathers references from the internet, creates an outline, and then produces the full article. STORM improves content quality through perspective‑guided questioning and simulated dialogue, and supports custom retrievers and models for different scenarios.

3. How to Use STORM

You can clone the repository locally (setting

openai_api_key

) or directly try the web version. The workflow includes:

① Set the topic and describe the writing purpose – for example, topic “code visualization” and purpose “introduce core concepts and frontier applications”.

② Automatic internet retrieval of relevant materials

③ Generate article content with the LLM

④ Optimize content through simulated dialogue

⑤ Display the generated content, which can be downloaded as a PDF

4. Underlying Principles

STORM generates cited long‑form articles through two main stages:

Pre‑writing stage : collects web references and creates an outline.

Writing stage : uses the outline and references to generate the full article with citations.

STORM’s advantages stem from automation, especially automated question generation. To improve question depth and breadth, it employs two strategies:

Perspective‑Guided Question Asking : discovers different viewpoints from related articles and uses them to steer questioning.

Simulated Conversation : mimics a dialogue between a Wikipedia author and an internet expert, allowing the model to update its understanding and ask follow‑up questions.

5. Conclusion

Using STORM for technical research feels comfortable; the generated article can be read and vetted (LLMs may hallucinate). This greatly improves efficiency, and in the era of large models, not knowing how to learn or search is mostly a matter of laziness.