The Best Way to Read a Book (That Nobody's Doing)

2026-01-20 · Source: Jeremy Howard · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Data Science & Analytics · Depth: Intermediate, extended

Summary

This discussion details an LLM-assisted reading process designed to enhance comprehension and engagement with complex books, particularly a new business book by Eric. The method involves using an AI tool, Sol, to generate chapter summaries, consolidate them into part and full book summaries, and provide extensive contextual information including footnotes, author discussions, and related external content. This preparatory work, which takes approximately two hours, creates a "customized workspace" for the reader. The process allows for deep dives into specific topics, challenging author claims, and exploring "rabbit holes" that enrich the reading experience. The authors emphasize the importance of providing the LLM with ample context and the ability to iteratively refine summaries and analyses, leading to a more interactive and profound understanding of the material.

Key takeaway

For AI Engineers or advanced readers seeking to maximize comprehension of dense texts, you should adopt a structured, LLM-assisted reading workflow. Invest the initial 2-3 hours to prepare a rich contextual environment for your AI, including chapter summaries, author insights, and external references. This upfront effort will transform your reading into an interactive dialogue, allowing you to challenge assumptions, explore tangential topics, and achieve a significantly deeper understanding than traditional methods.

Key insights

Providing extensive context to LLMs dramatically enhances their ability to support deep, interactive reading and analysis.

Principles

• Context is paramount for LLM performance.
• Iterative refinement improves AI-generated summaries.
• Customized workspaces boost reading effectiveness.

Method

The process involves pre-summarizing book sections, consolidating summaries, and feeding the LLM with comprehensive context (summaries, discussions, footnotes) to enable interactive questioning, fact-checking, and exploration of related concepts during reading.

In practice

• Pre-summarize book chapters with an LLM.
• Use XML tags for in-context learning.
• Integrate author discussions and footnotes as context.

Topics

• LLM-Assisted Reading
• Context Management
• Human-AI Interaction
• Information Summarization
• Close Reading

Best for: Prompt Engineer, AI Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Jeremy Howard.