How To Use AI for the Ancient Art of Close Reading

2026-01-20 · Source: fast.ai—Making neural nets uncool again – fast.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

The article details a novel "close reading" technique that integrates Large Language Models (LLMs) to enhance comprehension and engagement with complex texts. This method, demonstrated using the SolveIt platform, involves preparing text (e.g., converting PDFs to Markdown), generating chapter summaries for LLM context, and engaging in an interactive dialogue with the LLM. Users can ask clarifying questions, explore tangential "rabbit holes," personalize content, and even create Anki flashcards for spaced repetition. Examples include reading Eric Ries's book "Incorruptible" and Yann LeCun's "LeJEPA" academic paper. The process, while requiring an initial setup investment of about two hours, significantly deepens understanding by providing immediate context, counterexamples, and literary analysis, transforming reading into a highly interactive and personalized learning experience.

Key takeaway

For research scientists or advanced students tackling dense academic papers or complex books, integrating LLMs into your reading workflow can dramatically deepen comprehension and retention. You should invest time in setting up your LLM environment with comprehensive context, including book summaries and previous discussions, to enable rich, interactive exploration and personalized learning, rather than relying on superficial scanning.

Key insights

LLMs can transform traditional close reading into a dynamic, personalized, and deeply engaging learning experience.

Principles

• More LLM context yields better results.
• Grounding LLM answers prevents hallucination.
• Initial setup investment enhances long-term effectiveness.

Method

The SolveIt process involves converting PDFs to Markdown, generating chapter summaries, instructing the LLM to avoid spoilers, asking questions during reading, and generating conversation overviews for subsequent chapters. Optional steps include LLM-led comprehension checks and Anki card creation.

In practice

• Use LLMs to clarify complex terms or concepts.
• Explore related topics via LLM-guided "rabbit holes."
• Personalize learning by applying text principles to your context.

Topics

• Large Language Models
• AI-Powered Reading
• Context Management
• Personalized Learning
• SolveIt Platform

Best for: AI Student, Research Scientist, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by fast.ai—Making neural nets uncool again – fast.ai.