JevMade field notes / Technical guide

Map meaning across a long document

Semantic Microscope draws a map of sentence-level judgments, helping readers inspect a long document without replacing its text with an AI summary.

Original by abhishekmishragithubEvaluation

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Credits and license

“Semantic Microscope” by abhishekmishragithub. Read the original source.

This expanded guide is an AI-narrated adaptation of the source’s essential explanation, examples and caveats, not a word-for-word reading. The synthetic voice does not imitate the author or imply their endorsement.

License: MIT

MIT License

Copyright (c) 2026 Abhishek Mishra

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Our summary

Semantic Microscope splits a document into sentences and asks several questions about each one. It draws the answers as coloured lanes or blocks while keeping the original sentences available. A reader can look for patterns in contracts, stories, or technical documents without relying on a generated summary.

Saved question sets can look for features such as obligation or tension. The program saves answers as they arrive and records failures, allowing interrupted work to resume. A checking tool hides the AI's guesses while a person labels selected examples, then compares those labels with the probabilities.

The author's accuracy check covers only one document, question, and reviewer, and reports problems with middle-range probabilities. Sentence splitting and removed text also affect the picture. Documents go to the model, where hostile instructions may influence answers, so private or untrusted material needs care.

Key takeaways

Keep the full source visible beside the AI's labels.
Record failed and skipped sentences instead of silently dropping them.
Label your own examples without seeing the model's guesses, then compare the answers.

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