Yes. In the workflow described in a September 2026 Dev.to post titled “My notes became a personal RAG with no embeddings,” the notes never passed through an embedding index. The author kept personal Markdown notes, searched them from the terminal with a text tool, read the matching lines, and pasted the passages they chose into an AI prompt. The retrieval step was a literal text search followed by a person’s judgment about what was relevant.
This account relies on a secondary summary of that post by Donweb, which identifies the original as dated September 16, 2026. The original post was not checked directly, so the details below are attributed to that summary and are not independently confirmed tests.
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What retrieval-augmented generation means in this case
Retrieval-augmented generation (RAG) means finding material outside a model and supplying it as context so the model’s answer draws on that material. Vector embeddings are one way to find the material. They turn passages into numerical representations and rank them by similarity. Literal text search followed by human review is another way to select the same kind of context, and it is the approach the Donweb summary describes.
That distinction matters because many explanations of RAG assume the retrieval step is automated. In the reported workflow, the person doing the searching is also the person deciding what the model sees.
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The reported workflow, step by step
The Donweb summary describes a short sequence that any person with a folder of text files can reproduce:
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Keep notes as plain text. The notes are Markdown files in an ordinary folder. The summary’s example uses a directory named
notes/, and no database or index is involved. -
Search for the words you expect you wrote. The example command is
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Read the matches. Each result shows the file path and the matching line, so you can see the surrounding topic without opening every file.
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Choose the passages that matter. You keep the lines that answer your question and discard the rest.
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Paste the selected passages into the prompt. You add your question and the chosen text to the AI conversation, so the model answers from your notes.
The Donweb summary’s title refers to grep, but the command it shows is ripgrep. Either tool performs the same basic function of matching text, and the ripgrep flags above apply only to ripgrep.
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What the method buys
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No index to build or maintain. New notes are searchable as soon as they are saved to the folder.
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Visible inputs. You can see exactly which lines reach the model, which makes it easier to notice when context is wrong or thin.
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A human check before the prompt. The summary presents review as a feature: a person reads each candidate passage before it is used.
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Portable notes. The files remain ordinary text and stay usable if you stop using the search tool or the AI tool.
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Where literal search breaks
Literal search can only return notes that share words with the query. If the note says “rollback” and you search for “undo,” the note may never appear. The same problem occurs with synonyms, paraphrases, abbreviations, older terminology, and notes written in a different language from the one you remember.
The failure is hard to see because review covers only what the search returned. A relevant note that was never matched does not show up as a gap; it simply does not appear. Mitigation is mostly a habit: search several variants of a term, search for tags or headings you consistently use, and check file titles when a result set looks suspiciously small.
Literal search or a semantic retrieval layer
The useful comparison is not AI versus no AI. It is which retrieval method fits your notes, your memory of their wording, and how much review you are willing to do. The table compares the two approaches on the axes that matter most in this case. Entries for the semantic option describe how it generally works; the Donweb summary does not measure either approach against the other.
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| Axis | Literal search with manual selection (as described) | Semantic retrieval layer |
|---|---|---|
| Query vocabulary | Works when you can recall or anticipate the words the note uses | Can connect related ideas written in different words, though it can also return loosely related text |
| Corpus scale and review effort | Works while matches stay quick to scan; the summary gives no threshold | Typically adopted when volume makes manual review slow; not measured in the summary |
| Who searches | Best suited to the person who wrote the notes | May help someone who did not write the notes; not tested in the summary |
| Human oversight | Built in, because you read every match before it reaches the prompt | Ranking is automatic; review is optional unless you add it |
| Maintenance | No index to build; nothing to update when notes change | Requires an embedding step and an index that must stay in sync with new or edited notes |
When to add semantic retrieval
The Donweb summary lists conditions that would justify moving beyond literal search. Treat them as reasonable triggers reported in that account, not as universal thresholds:
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You repeatedly know a note exists but cannot recall the wording to find it.
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Related ideas in your notes are written with different vocabulary.
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The collection has grown too large to review matches by hand.
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Other people need to search notes they did not write.
A hybrid option to evaluate
If you need more than literal search, the lowest-risk path is to keep the search step and add a semantic fallback only when it is justified. Record the cases where a note existed but your search missed it. If those misses are rare, the simpler method is probably enough. If they recur, a semantic layer addresses a demonstrated problem rather than a hypothetical one. The Donweb summary describes a fallback of this kind as the author’s contemplated next step. It is not established as built or effective.
Recommendation
Start with the simplest retrieval method that works for your notes: plain-text files, a literal search tool, and your own review of each match. Add semantic retrieval when real misses show that literal search is the bottleneck. This is a practical synthesis of the Donweb account rather than a finding from a controlled comparison.
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