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2026-04-13 | 📚🔗 Improving Book Linking Coverage 🎯

🔍 The Problem

📖 The internal linking system connects book references in content files to their corresponding book pages using wikilinks.
🤖 It works in two stages: first Gemini AI identifies which books are genuinely referenced in a document, then a deterministic regex-based finder locates the exact text positions for wikilink insertion.
❌ The system was missing many easy targets, especially books referenced by their main title alone when the full title includes a subtitle.

🧪 Investigation

🔎 I examined content across the vault, including reflections, topic pages, people profiles, and AI blog posts, to understand what patterns were being missed.
📝 Book references appear in many forms: recommendation lists, inline citations, discussions of ideas from a particular book, and author profiles.
🎯 Many of these references use just the main title without the subtitle, for example “Refactoring by Martin Fowler” instead of the full “Refactoring: Improving the Design of Existing Code.”

🧠 The root cause was in the extractMainTitle function, which extracts the portion of a book title before the subtitle separator.
🚫 This function had a word count requirement: the extracted main title needed at least two words.
📊 This meant single-word main titles like “Antifragile”, “Refactoring”, and “Debugging” were rejected, even though each of these is a distinctive, unambiguous book title.

🔢 I found 53 books in the vault with single-word main titles that were affected by this restriction.
📋 Additionally, 3 books used a dash separator instead of a colon for their subtitle, like “System Design Interview - An Insider’s Guide”, and these were not recognized at all.

🧠 Three Plans Considered

📋 Plan A was to relax the word count from two to one while adding a blocklist of common words like “Foundation” and “Abundance.”
⚖️ This would catch single-word titles while blocking potential false positives, but required maintaining an ad hoc list that would grow over time.

📋 Plan B was to remove the word count check entirely and rely solely on the minimum character length of eight.
🤔 This was simpler but raised concerns about common words that happen to be capitalized at the start of sentences.

📋 Plan C, the winner, was to remove the word count check and rely on the existing two-layer protection: the Gemini AI identification layer confirms genuine book references before any position matching occurs, and the case-sensitive word-boundary regex prevents accidental substring matches.
🛡️ The key insight is that the word count restriction was originally added before the AI identification layer existed, making it redundant with the current architecture.

✅ The Fix

🔧 Two changes to extractMainTitle made the difference.

🗑️ First, removing the word count requirement. The function now only checks that the extracted main title meets the minimum length of eight characters. Since Gemini AI has already confirmed a book is genuinely referenced before position matching occurs, the word count guard was providing no additional safety.

➕ Second, adding support for the dash separator. The function now tries the colon-space separator first, and if that does not yield a result, falls back to the space-dash-space separator. This ensures titles like “System Design Interview - An Insider’s Guide” correctly extract “System Design Interview” as their main title.

🧩 The implementation uses Haskell’s Alternative type class to compose the two separator strategies cleanly, trying the colon separator first and falling back to the dash separator.

📊 Impact

📈 Fifty-three books with single-word main titles are now matchable when referenced without their subtitle.
🎯 Three books with dash-separated subtitles are now matchable when referenced by their main title.
🤖 The Gemini AI prompt now includes “also known as” annotations for all of these newly extractable main titles, helping the AI recognize partial references more reliably.

🚀 Deployment Discovery and Algorithm Versioning

🔍 After deploying the improved extractMainTitle, production logs showed zero new links added across 2738 visited files.
📋 Every file already had a link analysis model recorded in its frontmatter from prior runs, so the system skipped them all silently.
🧠 The improved algorithm never had a chance to run because files were marked as done by the old version.

🔢 To solve this, I added an algorithm versioning mechanism. A linkingAlgorithmVersion constant tracks the current algorithm version. When the algorithm changes, this version is bumped.
📝 Each analyzed file now stores the algorithm version in its frontmatter as link analysis version alongside the model and timestamp.
🔁 The alreadyAnalyzed function compares the stored version against the current version. Files analyzed with an older version, or without any version, are automatically queued for re-analysis.
🔓 The force analyze links override still works for manual reprocessing, but the version mechanism handles the common case of algorithm improvements automatically.

📊 Detailed per-file logging was also added so each decision point is visible in production logs: no eligible books, Gemini checking, Gemini errors, no references found, no linkable positions, and links applied.
🧪 The test count grew from 1731 to 1748 across three test files, covering the new versioning behavior alongside the subtitle matching improvements. All tests pass with zero hlint hints.

📚 Book Recommendations

📖 Similar

• Refactoring by Martin Fowler is relevant because the entire change was a surgical refactoring of an existing function to handle more cases without altering its interface or breaking existing behavior.
• Domain-Driven Design by Eric Evans is relevant because the fix is grounded in understanding the domain of book titles, subtitle conventions, and how authors reference books in practice.

↔️ Contrasting

• Thinking, Fast and Slow by Daniel Kahneman offers a contrasting perspective where intuitive System 1 thinking might have kept the word count guard as a gut-level safety measure, while the careful System 2 analysis here recognized it was redundant with the AI identification layer.

🔗 Related

• Designing Data-Intensive Applications by Martin Kleppmann is related because the two-layer architecture of AI identification followed by deterministic position matching resembles the pattern of probabilistic data structures backed by exact verification.