AI Embeddings Explained Simply: How Software Finds Related Ideas

You search for help about cannot log in on a help page. The phrase used by the page is sign-in problem, so an exact word search might miss the page. But, you can tell that both phrases have to do with a similar issue. AI embeddings can allow software to find documents with similar ideas despite having different wording.

An embedding is a sequence of numbers that is generated from an input, which could be a text. These numbers allow a machine to compare things. They are not a correct representation of meaning and they are not verification that the document is right. You can view them as a helpful search tool with limits.

This article will discuss embeddings without involving any complicated mathematics. We will consider a small library and a business help desk as examples. The purpose of this discussion is to learn about related searches, useful results, and the verification that should be done about those. This is an example of planning and is not a statement on a functioning Digitonix solution. It is possible to read this article without having coding skills.

Start with the library example

Let us consider a library where there are books on plants, cooking, sport, and computer science. A person is able to categorize the books by topic. Software needs some means of comparing the information. An embedding model turns text into a number list that helps software compare items.

They are known as a vector. In this case, the vector means an ordered list of numbers. It is not required that you calculate the vector manually. AWS has documentation on text embedding where one such model is described. In practice, the important thing is whether the tool makes finding the necessary information easier.

Understand related search

The related search is sometimes called a semantic search. Semantic refers to meaning. The application compares the question with the saved records and finds the useful matches. Even if the wording is not the same, a question like "resetting a password" could give you the sign-in instructions. The result depends on the model, documents, and the search build in general.

But, do not assume that all such concepts will be easily recognizable. A brief expression may have a number of interpretations. Bank can mean a money service or the side of a river. A good search system should include context and tests that will provide meaningful results. A display of a near-matching concept is only the first stage of assisting the user in completing his task.

Keep exact facts in view

Certain searches may require an exact value. Order ID, SKU or account ID cannot be seen merely as a broad concept. The application might require both exact and related searches. The choice of the approach depends on the question. A system capable of finding similar orders may not be enough when the user requires a certain order.

In the help centre, a search for sign-in help may be done using related meanings. In the case of searching for a specific product version, there is a need for exact filters. The two can be combined. Test both approaches using realistic questions before designing the system. The company needs to decide on whether the output aids the task, not whether the search has used the latest terminology.

Prepare the documents carefully

Use helpful and current documents. A search system cannot transform an unclear old policy into a clear current one through embeddings. Find out who owns the document. Remove duplicate documents, where necessary, and include the date or version. Users must know the document and whether it applies to their query.

The use of good structure is also helpful. Make sure you use appropriate headings and group all the related text. One file can contain several different subjects. The system may split the text into smaller parts for search. Try to organize your file in such a way that it will preserve all the necessary context. The mention of a fee can be misleading if the condition affecting this fee was stored in a different part.

Connect each result to its source

Try to keep the relationship between the stored number lists and the text that is tied to them. The output from the search process should contain the name of the source, the source itself and the corresponding text. There is no need to display the list of numbers. In such a manner, the user will be able to get access to the source document.

Source links also help the team to develop better search features. When people keep on getting an outdated guide, then the owner should check the file stored and the reason why it is still there. It should not be assumed that when a document is updated, everything will get updated in the search. The application should have a way of adding, changing, and deleting documents in its search database.

Do not confuse similarity with truth

The similarity score does not show the probability of being right for the document and the answer itself. The incorrect policy can turn out to be very similar to the question. Besides the matching mechanism, there should be other measures for checking sources, policies and the answer's correctness.

Decide how to deal with unclear or poor results. The application can use a follow-up question, present a small list of references, or redirect the user to a contact person. Don’t invent a fact only because a result has to be shown at the top of the list. The user must know what result is useful and what is still not enough for a safe answer.

Check access before using private text

The search function has to adhere to the same access policy as the rest of the application. A staff member cannot access any private data that he or she doesn’t have the right to see. Access control is required for the search path. An unusual request cannot become an access violation for the other team’s restricted data.

Add previews, source snippets, and answers by the AI to your analysis. Personal information can be gleaned from a small result without viewing the source document itself. Embeddings are not an automatic protection measure against privacy either. Look at the storage and use of embeddings in your data strategy. Secure the full information chain, not just the web page where the original is stored.

Understand the link with Gen AI

The search system may provide the allowed text for the Gen AI algorithm to use. This process is called RAG, retrieval-augmented generation. In other words, the app retrieves valuable information and then asks the AI to use it. Searching and answering are two different processes that are tied to their own mistakes.

Double check each step. Was the right allowed document found through the search? Was the document used by the model in the right way? Is the answer related to useful evidence? It does not matter how good the search result was because that does not mean the generated reply was correct. The application should inform users of any missing information and allow them to view useful information.

Test questions that are easy and difficult

Create a small set of test questions. Include popular wording, alternative wording for the same purpose, unclear terminology, and clear product names. Include one question that has no valid answer among the collection. Specify which source should be found and what users should see when there is no useful information available.

Ask people to check the usefulness of search results and do not just consider a technical score. A correct document might be hidden behind several wrong matches. A result might be useful but too outdated to use. Take note of this and revise the source or the design of the search. Repeat testing after making large changes, as a new model or updated collection might affect what is retrieved.

Keep model changes under control

The number lists depend on the method you use to make them. You cannot make assumptions that numbers from different models can be compared in a useful manner. A model change may need new number records and a planned update to the search collection. Your group should be aware of the method used and the procedure for changing the list.

Plan the switch ahead of time and test the new search with a sample collection and compare the results with the current task. Document the known limits and the recovery options. A new search must not silently disable source links or show a different set of private documents. The search collection is part of the application, and there needs to be ownership and releases.

Count useful work and ongoing effort

Look if users are able to get the proper information and accomplish their task. It can be a record of repeated searches, unanswered questions, and access to helpful sources from a help center. Mix it with the feedback you have got. More common searches may show valuable searching, but more common searches may show uselessness of the first results.

Include in the cost assessment the aspects of document maintenance and storage, model requests and support functions. Someone will have to constantly keep the collection and sort out the problems with access rights and sources. A fast demo with just two documents that are free from errors doesn't show such work. Start from a smaller collection that your team can handle, then expand as needed.

How Digitonix can help

AI solutions and software development services offered by Digitonix may be used for business search and the associated AI-related functions. Provide sample documents allowed for use, common requests, source guidelines, and expected output. Request a limited search scope with source connections, effective tests and owner for updating. This information will make the plan easier to analyze.

Data analysis might be done with Python, while for some other purposes PostgreSQL would suit. It all depends on the kind of the project you are going to do and on its size. Please contact us through Digitonix and we will discuss useful search results and possible limits ahead of time. What is important is to make available for people allowed, current information, not just some vector database in the project.

Frequently asked questions

Is an embedding the same as a written answer?

No. An embedding is a number list used for comparisons. A written answer is text a person reads. Search can use embeddings to find source text. A separate step may create an answer. Check both steps. Finding related text does not prove that a reply uses the right facts.

Does a high similarity score mean the result is true?

No. This shows a comparison between the search approach rather than the test for the validity of a document. An incorrect or old document can still be related to the question. Check the source quality, dates, business rules, and access. Describe what the score means and ensure that there is a way back to the original document before a decision is made.

Should we replace every exact search with semantic search?

Select the search technique for the task. Useful searches can be helpful in finding documents using alternative wording, whereas precise values like product codes may require an exact match. A combination of both designs would be helpful. Test with realistic inquiries and see what the user will really be able to find. Do not modify the search technique just because of a new name.

Can embeddings make private documents safe to share?

Don’t take them as a blanket solution to privacy problems. Review the stored number lists, source text, previews, and AI answers as parts of one data path. Make sure to apply the proper access restrictions and provider obligations. The system must be secure for the information in both the search and answer stages, rather than depending on a harder-to-understand form to do the job.

What is a good first embedding project?

Select a small, curated set of documents and a definite search problem. Provide common queries, likely sources, no answer cases, and access restrictions. Assign an owner for updates. A good pilot application will prove that people can discover and check the right information, with enough proof of a decision on expanding the document set.