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Documentation Index

Fetch the complete documentation index at: https://www.doc-reviewer.site/llms.txt

Use this file to discover all available pages before exploring further.

Doc Reviewer is a desktop application that automatically scans your technical documentation — PDFs, Word documents, Markdown files, and web pages — detects instruction sections, and evaluates each one against a configurable set of quality criteria using large language models. Every instruction gets a color-coded result (green, yellow, orange, or red) along with specific, actionable recommendations to help you improve your documentation.

Installation

Download and set up Doc Reviewer on Windows, connect your LLM, and run your first evaluation.

Quick Start

Upload your first document and get evaluation results in minutes.

Core Concepts

Understand projects, documents, evaluation results, and criteria sets.

Configuration

Connect OpenAI, Anthropic, Ollama, or any OpenAI-compatible model.

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How it works

1

Install and launch

Download the doc-reviewer.exe and run it. The app starts a local server and opens in your browser automatically.
2

Connect an LLM

Go to Settings → Models and add your LLM API key or configure a local model via Ollama.
3

Create a project and upload documents

Create a project for your product, then upload PDF, DOCX, Markdown, or TXT files — or load pages directly from a URL.
4

Run evaluation and review results

Click Evaluate to analyze all instruction sections. Review color-coded results and recommendations, then export to XLS.

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Key features

Multi-format document support

Parse and evaluate PDF, DOCX, Markdown, and TXT files. Load web pages by URL using a headless browser.

Automatic instruction detection

Doc Reviewer automatically identifies instruction sections in your documents using morphological analysis — no manual tagging required.

Configurable evaluation criteria

Use the built-in criteria set or create your own. Edit criteria directly in the UI using Markdown format.

Version comparison with snapshots

Save evaluation results as snapshots and compare them across document versions to track quality improvements over time.

Product context for better accuracy

Attach a product context to each project so the LLM understands your product’s terminology and audience when evaluating instructions.

Local and private

All data stays on your machine. Doc Reviewer uses a local SQLite database and runs entirely offline except for LLM API calls.