Start with questions your team already answers
This guide is for teams that repeatedly search manuals, procedures or approved internal documents before answering a colleague or customer. A useful starting point is one recurring question type and a document collection with an owner.
For example, an internal support colleague might need the current procedure for a particular product and error code. A proposed assistant could find the relevant passage and prepare an answer with a citation. If the product, document version or permission is uncertain, it should ask for clarification or hand the case to a person. This is an illustrative design, not a customer outcome.
When ordinary search may be the better first step
If the answer is easy to find in a small, current handbook, improving its navigation may be enough. If documents conflict or nobody owns them, resolve those issues before adding an answer generator.
Consider a pilot when people repeatedly need to find and combine approved information, and you can define a supported answer. The pilot should test whether the extra answer layer helps compared with your existing search; it should not assume that chat is automatically better.
RAG means search first, answer second
RAG stands for retrieval-augmented generation. In everyday terms, the system searches a set of documents first and gives the useful parts to the AI before it writes an answer. The original research paper and Microsoft's product guidance describe this same basic pattern.
Search gives the AI more useful information, but it does not solve everything. The system still has to know which document is current, who may read it, which product the question is about and what to do when two sources disagree.
Compare ordinary search and AI answers on the same question
Fictional example: a colleague asks, ‘Which form do we use to request a loan laptop?’ An approved IT handbook, version 3, says to use form IT-12. An archived handbook points to IT-04. This is an invented document set to illustrate the decision; it is not a customer deployment.
A source-backed draft might say: ‘Use IT-12, according to the current IT handbook, version 3, Equipment loans.’ It should link to the actual passage the reader may access. A plausible-looking citation alone does not establish that the answer is supported.
| Situation | Ordinary search | AI answer layer |
|---|---|---|
| Current, accessible handbook | Return the relevant passage for the person to read. | Draft IT-12 with the supporting passage and version. |
| Old and new instructions | Show version and archive information clearly. | Use the approved current version; stop if authority is unresolved. |
| No supported answer | Show no relevant result or broaden the query. | Say the answer was not found and route the question to an owner. |
| Restricted source | Exclude documents the user cannot access. | Do not reveal the restricted content through an answer or citation. |
Choose navigation, search or an answer layer
Improve navigation first when staff repeatedly need the same few pages. Consider search across approved sources when the problem is finding documents in different locations. Test an AI answer layer when staff must interpret or combine passages and can verify the answer against them.
Try the same representative questions with your current search and the proposed workflow. Record whether people found the right source, whether the answer is supported, review time and unanswered cases. Include outdated, missing and restricted material. Do not expand to more document libraries until those boundaries work.
Good search does not guarantee a good answer
Microsoft evaluates the document search and the final answer separately. This matters because an answer can sound convincing even when the search missed the right document. The opposite can also happen: the system finds the right page, but the answer adds a step that the page does not support.
NIST also lists made-up content, information quality and the way people work with AI as risks to manage. For technical support, important or risky answers should pass automatic checks and a human review.
What a real support tool needs
A real RAG tool needs more than a search box and chat screen. The team should be able to answer these questions clearly.
- Which document libraries, versions and languages may be searched?
- Which documents may each user see?
- How should product codes, keywords and meaning-based search work together?
- What happens when a document is old or two documents disagree?
- Which source passage supports each important sentence?
- When should the tool ask a question or send the case to a person instead of answering?
- Which real support questions will be used to test the tool?
What the demo shows and leaves out
The demo searches by product and error code, checks whether a document is current and allowed, shows the selected passages and stops when key information or permission is missing.
It uses five preset cases. It does not import changing documents, split them for search, run a live AI model or connect to a company login system. A real pilot must add those parts and test them with questions and documents approved by the customer.
Start smaller than a chatbot
Choose one support queue, one well-managed document library and one type of answer. Check whether people agree that the tool found the right documents. Then check every important sentence against its source and record every case the tool could not answer. The goal is not to answer everything. It is to be dependable within a small, useful area.
Apply this to one workflow
Still finding a starting point? We can help you identify opportunities. If you already have one task, use the free check or explore a scoped pilot. No files or customer data are needed for an initial inquiry.
Related guides
Sources
- NeurIPS 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- Microsoft LearnRetrieval augmented generation and indexes in Microsoft Foundry
- Microsoft LearnRetrieval-Augmented Generation evaluators
- NISTArtificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile
Sources support the specific statements linked above. They do not establish that a tool or reference workflow will fit a customer's systems and data.