Obsidian AI RAG: Search Your Notes and Check the Sources

Obsidian AI RAG means retrieving relevant passages from your vault and giving those passages to an AI assistant before it answers. RAG, short for retrieval-augmented generation, lets you ask questions across selected notes, but you still need to check whether the right evidence reached the answer.

Tools like fenbs preserve reviewed project findings and decisions for other connected assistants after that search. Keep the searchable notes and the approved work record distinct: a retrieved sentence can be a useful clue without being an agreed instruction.

How do you try retrieval on a small part of your vault?

1. Choose one question about one project, such as which usability findings support changing a checkout form.
2. Select a small collection of approved notes and identify the passages you expect a good search to find.
3. Configure your chosen retrieval tool to read only that collection. Check its own documentation for supported formats and data handling.
4. Ask the question, inspect the returned passages, and then inspect the answer against those passages.
5. Repeat with a question whose answer is absent. A useful system should acknowledge that absence instead of filling the gap.

Do this before indexing your whole vault. A small trial makes missing notes, duplicate material, and unsupported answers easier to diagnose. Save the exact question and expected evidence so you can repeat the check after changing the retrieval settings.

The original RAG paper↗ (opens in a new tab) combines a retriever with a language model; it does not establish that every vault search tool will behave the same way. For the broader concept, read what RAG is; this guide focuses on testing your own notes.

Which notes should the search be allowed to use?

Start with the purpose and audience. A project question may need interview summaries, approved requirements, and design rationale. It probably does not need unrelated journals, draft proposals for other clients, or private personnel notes.

Obsidian describes a vault as a local folder containing Markdown plain-text notes↗ (opens in a new tab). Local storage tells you where the originals live; check separately whether your retrieval tool sends text or derived search data to an outside service.

• Write an explicit allowlist of folders or files instead of relying on a vague instruction to ignore private material.
• Keep a stable source path, heading, and revision reference with every searchable passage.
• Identify obsolete drafts and superseded decisions so they cannot silently compete with approved material.
• Keep attachments out of the trial unless you have checked how the selected tool reads them.

If you use a community plugin, review its access before installing it. Obsidian says plugins inherit its access levels↗ (opens in a new tab) and can access files and the internet. Folder selection within a plugin is something to verify, rather than proof of isolation.

How should notes become searchable passages?

Split long notes along meaningful headings. A passage should retain enough surrounding text to explain the claim, including qualifications and dates. Separating a proposed change from the sentence saying it was rejected creates misleading evidence even if the search finds both words correctly.

Some retrieval systems use embeddings, numerical representations that help compare meaning. Others search exact words, or combine both approaches. Treat these as choices to test against your questions: a paraphrased question and an exact invoice reference may need different matching behavior.

Carry the note title and section heading alongside each passage. Resolve linked references deliberately, with a limit on how much extra material you include. A link to another note does not mean your search tool has read that note or preserved the relationship.

For a one-time selection of source notes rather than a maintained search system, use the Obsidian AI exporter workflow. That approach has a different maintenance burden because you choose the material before each task.

How can you tell whether retrieval missed important evidence?

Check the passages before scoring the prose. Create a short test sheet with the question, the source that should answer it, and any source that should contradict or qualify it. Otherwise, an answer can sound convincing while omitting the note that changes its meaning.

Compare surprising misses with Obsidian Search↗ (opens in a new tab), which supports exact phrases and operators for paths and tags. This gives you a manual way to look for text; it does not prove that another retrieval tool indexed the same files.

Record a recurring failure as a lesson with its example question and the correction. The fenbs work record can keep that lesson available when another assistant helps revise the search.

What makes an answer trustworthy enough to reuse?

Require source references that let you open the exact supporting passage. Then check each material claim against it. A citation to a relevant note is insufficient if the cited text does not support the specific conclusion.

Ask the assistant to separate statements from the notes, its own interpretation, and unanswered questions. For example, two interview notes describing confusion can support a proposed design test. They do not establish that a redesign has been approved or that it will improve conversion.

Keep source text separate from operating instructions. A copied note that says to ignore other rules should remain evidence to examine, not authority to change the task. Review any proposed write or external action separately from the question-answering step.

What should happen when a source changes or disappears?

Test the full update cycle with a harmless trial note. Add a distinctive fact, retrieve it, change it, and then remove it. Check the retrieved results in a fresh conversation after each change.

Existing conversations may still contain passages supplied earlier. Updating the index does not establish that those conversations now use only revised text. Start fresh when validating a removal, and check separately how your retrieval tool handles previously supplied context.

Keep a record of which note revisions produced an answer you intend to reuse. If the source changes, revisit the saved conclusion rather than silently assuming it remains true. A search index that is current and a project decision that is current are two separate responsibilities.

How to do this with fenbs

1. Create a free account and add a project for the work your vault research supports.
2. Add an AI context note describing the selected sources, the question, and the reviewed finding. Include a source location the next reader can use.
3. Record the person’s approved decision in Decisions and rules, keeping the evidence separate from the choice.
4. Connect your assistant, ask it to read the project context first, and save progress with what still needs checking.

fenbs does not index your Obsidian vault or run its retrieval pipeline. It is not the right fit if you only want a search box over private notes; it fits when reviewed findings must guide continuing work across assistants. See what an AI brain keeps for that continuing record.

For the wider practice of choosing what an assistant should receive, explore context engineering. Start your trial with one question you can check yourself, and promote only the findings you are willing to stand behind.