What embeddings are and how they help find information
Embeddings are numerical representations that help a system compare content. In text search, they can bring a question closer to documents about related ideas, even when their wording is not identical.
That does not turn similarity into truth. A passage close to a question can be useful for retrieval while still lacking the correct answer for the user’s situation.
Imagine a library of procedures
Consider three fictional documents: instructions for changing a password, rules for exchanging a product and guidance about blocked access. Similar words can appear in very different subjects.
A search using numerical representations may help connect “I cannot sign in” with an access document. Actual results depend on the model, content preparation and retrieval rules.
Microsoft’s vector search overview explains retrieval through similarity between numerical representations. That is the basis of our example, not a promise that every search will find its intended document.
1. Separate text, vector and answer
Text is the content people read. An embedding represents that content as a sequence of numbers used for comparison.
A written answer is another stage, where an application uses retrieved results to respond. Embeddings by themselves do not write the final explanation.
| Element | Role in the exercise |
|---|---|
| Document | Store the access instruction |
| Embedding | Enable numerical comparison |
| Retrieval | Select relevant candidates |
| Answer | Explain using the retrieved passage |
You can use embeddings for search without building a conversational assistant. Likewise, a conversation with AI does not prove that vector search took place.
2. Create query and document pairs
Before choosing tools, write questions your users would ask. Associate each question with the document that should be found.
Include different words for the same need and similar expressions with different meanings. That makes a test more informative than repeating a document title in every query.
Prepare example queries to test a procedure search.
For each supplied document, suggest a direct question and a paraphrase.
Include a query with a similar word but a different intention.
Do not classify actual results: we have not run the search yet.This prompt helps assemble a test collection. Comparison with real results comes only after your retrieval system is working.
3. Keep representations compatible
Microsoft’s embedding generation documentation recommends the same model for indexing and querying in that workflow. Mixing incompatible representations prevents a coherent comparison.
If you change the model used for the collection, plan how to update previously stored vectors. Do not assume that the label “embedding” makes every vector interchangeable.
Record the model and preparation method used during each experiment. Otherwise, a change in results may be difficult to explain later.
4. Inspect the retrieved content
Assess whether the passage resolves the question instead of looking only at a score. The similarity scale depends on the technique and is not a universal probability of correctness.
Exact terms, such as product codes, may need another strategy or a combination with keyword retrieval. The deciding factor is observed usefulness on your test questions.
Where this fits into RAG
In a RAG application, embeddings can help with document retrieval. Answer generation must still use context correctly and identify missing evidence.
To see where this stage belongs, read RAG explained through a practical example.
Frequently asked questions
What is an embedding?
It is a numerical representation of content used for comparisons, such as finding similarity between a question and documents.
Does an embedding write an AI answer?
No. It can help retrieve content. Writing the answer is a separate application stage.
Does a high similarity score guarantee a correct answer?
No. Check the retrieved content and its conditions. Similarity is not a universal probability of correctness.