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This page answers the most common questions about Doc Reviewer. If you are running into a specific problem — an app that won’t open, an evaluation that returns errors, or web pages that fail to load — the answers here cover the most likely causes and fixes. If your question is not listed, check Known limitations for constraints that may affect your workflow.
Doc Reviewer is a local desktop application, but it uses a web-based interface for its UI. When you launch doc-reviewer.exe, the app starts a local backend server on localhost:8000 and automatically opens the interface in your default browser. No internet connection is required to use the app itself. The only outbound network calls Doc Reviewer makes are LLM API requests to the provider you configure — OpenAI, Anthropic, or a local model via Ollama.
Your documents are stored entirely on your machine. When you run an evaluation, Doc Reviewer sends the text of each instruction section to the LLM API you have configured — nothing else. Your files remain in the local data/uploads/ folder next to the .exe, and all metadata, evaluation results, and settings are stored in a local SQLite database at data/db.sqlite. Your API keys are stored locally in that database and are never transmitted anywhere other than the LLM provider’s API endpoint.
For the most accurate evaluation results, use a capable frontier model such as GPT-4o (OpenAI) or Claude 3.5 Sonnet (Anthropic). If you prefer open-source models via Ollama, choose a model with at least 70 billion parameters — smaller models tend to produce less reliable criterion scoring and may give vague or inconsistent recommendations.
If evaluation results look inconsistent or clearly wrong, the LLM model is the first thing to check. Switch to a stronger model in Settings → Models and re-run the evaluation.
Yes. Doc Reviewer supports any OpenAI-compatible API, including local models served by Ollama. To configure a local model, go to Settings → Models, add a new model, and set the base URL to http://localhost:11434/v1. Leave the API key field blank — Ollama does not require authentication. Make sure Ollama is running with your chosen model loaded before you start an evaluation.
Web page support requires Chromium, which is not bundled inside the .exe. If you have not installed it yet, run the following command in a terminal:
py -3.11 -m playwright install chromium
If Chromium is already installed but the page still fails to load, the site may be blocking headless browsers. Some sites detect automated requests and return an error or an empty page. In that case, download the page as a PDF or DOCX and upload the file instead.
Several things can cause inaccurate evaluation results:
  • Weak LLM model — small or poorly-tuned models give unreliable scores. Switch to a stronger model in Settings → Models.
  • No product context — without context, the LLM evaluates instructions without knowing the product’s terminology and audience. Open your project page and generate or write product context, then re-run the evaluation.
  • Incorrect criteria — the active criteria set may not match your documentation style. Review or customize criteria in Settings → Criteria.
  • False positives — if a specific criterion is flagging things incorrectly for a particular instruction, mark it as a false positive using the override control in the evaluation result panel.
Yes, Doc Reviewer can evaluate documents in English. However, the built-in default criteria set and the LLM role description are written in Russian. For best results with English documents, go to Settings → Criteria, open your active criteria set, and translate the criteria text to English. You may also want to update the ## Роль (Role) section at the top of the criteria file, which is used as the LLM’s system prompt role.Automatic instruction detection uses morphological analysis optimized for Russian. Detection accuracy may be lower for English section titles — you can manually reclassify sections that are missed or incorrectly classified.
All data is stored in the data/ folder next to doc-reviewer.exe:
  • data/db.sqlite — the SQLite database containing all projects, documents, evaluation results, model settings, and criteria sets
  • data/uploads/ — the original document files you have uploaded
Do not move or delete these files while the app is running. To back up your data, copy the entire data/ folder.
To update to a newer version, replace doc-reviewer.exe with the new file. Your data/ folder — including data/db.sqlite and all uploaded documents — is preserved separately and carries over automatically. You do not need to redo any configuration.
If the database schema changes between versions, Doc Reviewer runs automatic migrations on startup. In rare cases where a migration cannot be applied, you may need to delete db.sqlite and reconfigure the app from scratch. Release notes will call this out explicitly when it applies.
Doc Reviewer does not have built-in team sharing. To share results, use one of these approaches:
  • Export to XLS — click Export on the evaluation page to download a spreadsheet with all results and recommendations. Share the file by email or in a shared folder.
  • Snapshots — save a named snapshot of the current evaluation state. Snapshots let you track quality over time and compare versions, but they are stored locally and cannot be shared directly.
Multi-user or server deployment is not supported.
Instruction detection uses morphological analysis trained on Russian-language text. It looks for patterns such as verb-noun combinations in section headings that suggest a procedural step. Sections with unusual, very short, or non-standard titles may be missed.If a section that should be evaluated is classified as non-instruction or possible, click the classification badge in the document tree and change it to instruction manually. You can also toggle individual sections in or out of the evaluation run using the include/exclude control next to each section.
Product context is a short text description (roughly 400–700 words) of a product — its name, audience, key terminology, and main components. When you set product context for a project, Doc Reviewer includes it in every LLM evaluation prompt for documents in that project. This lets the model evaluate instructions with knowledge of your product’s domain rather than guessing from the instruction text alone.Product context noticeably improves scoring accuracy for specialized or domain-specific instructions. You can generate it automatically from the non-instructional sections of your documents by clicking Generate context on the project page, or write it manually.