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, Data Science & Analytics, Software Development & Engineering · Depth: Advanced, extended

Summary

This content explores "close reading" with Large Language Models (LLMs) as a technique for deep textual analysis, emphasizing outward connections and holistic understanding. It details a process using the SolveIt platform, which integrates LLMs for interactive reading, allowing users to ask clarifying questions, explore rabbit holes, and personalize content. Two examples are provided: Jeremy Howard's reading of Eric Ries's book "Incorruptible" and Johno Whitaker's analysis of Yann LeCun's LeJEPA academic paper. Key benefits include enhanced comprehension, personalized learning paths, and the ability to generate summaries and Anki flashcards for spaced repetition. The process involves converting PDFs to Markdown, generating chapter summaries for context, and iteratively engaging the LLM. While acknowledging early-stage tool clunkiness and the hallucination risk, the authors highlight the value of extensive context setup for optimal LLM performance.

Key takeaway

For research scientists or AI students seeking to deepen their understanding of complex texts, integrating LLMs into your reading workflow can transform passive consumption into an active, exploratory learning experience. By investing a few hours in setting up a rich context for the LLM, you can ask targeted questions, explore tangential topics, and personalize the material, leading to significantly enhanced comprehension and retention. Consider platforms like SolveIt or similar tools that facilitate robust context management and interactive dialogue.

Key insights

LLMs can significantly enhance close reading by providing interactive context, enabling deeper understanding and personalized learning.

Principles

• Extensive context improves LLM performance.
• Iterative questioning deepens comprehension.
• Personalized application aids retention.

Method

The SolveIt process involves converting texts to Markdown, generating chapter summaries for LLM context, and engaging in iterative Q&A, optionally creating Anki cards and conversation overviews.

In practice

• Convert PDFs to Markdown for LLM input.
• Generate chapter summaries for context.
• Use LLMs for clarifying questions.

Topics

• Close Reading
• Large Language Models
• SolveIt Platform
• Context Management
• Spaced Repetition

Best for: AI Student, Research Scientist, Software Engineer

Related on AIssential

• How To Use AI for the Ancient Art of Close Reading — LLMs can transform traditional close reading into a dynamic, personalized, and deeply engaging learning experience.
• The Best Way to Read a Book (That Nobody's Doing) — Providing extensive context to LLMs dramatically enhances their ability to support deep, interactive reading and analysis.
• How do ML researchers actually use AI tools to improve their writing? [D] — LLMs offer efficiency in technical writing and coding but risk diminishing cognitive skills and originality if over-relied upon.
• Give Your AI Unlimited Updated Context — An LLM-maintained personal wiki creates a persistent, compounding knowledge artifact, eliminating repetitive context setup.
• Can Large Language Models Understand Context? — LLMs struggle with nuanced context understanding, especially pre-trained dense models and those with aggressive quantization.
• Leveraging LLMs in your Obsidian Notes — LLMs can transform note-taking by enabling conceptual search and conversational interaction with personal knowledge bases.

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