AI Research Assistants Using RAG: Test Sources and Citations

An AI research assistant using RAG retrieves relevant passages from a chosen collection before drafting its answer. RAG means retrieval-augmented generation: finding evidence and giving it to the model as context. Build the workflow around questions you can verify, then test whether it finds the right passages and cites them accurately.

Tools like fenbs keep the research requirements, source links, and decisions outside the retrieval system. That lets you change the search setup or the assistant without losing the reason a source was accepted, the questions still open, or the tests the next session must run.

How do you build a source-grounded research workflow?

1. Choose one research question and define an acceptable answer. State the audience, scope, required evidence, and situations where the assistant should say it cannot answer.
2. Collect a small set of approved sources. Keep each source’s title, original link, publication or revision date, and access restrictions with its text.
3. Make passages retrievable without losing their meaning. Keep section titles, nearby qualifications, and stable references so a result can be checked against the original.
4. Retrieve passages before drafting. Ask the assistant to connect factual claims to those passages, distinguish inference from evidence, and flag conflicting sources.
5. Test retrieval and the final answer separately. Save failed questions, inspect the evidence, and change one part of the workflow at a time before testing again.

Microsoft’s RAG overview↗ (opens in a new tab) describes the retrieve, augment, and generate sequence. Finding material supplies context; it does not guarantee a correct conclusion. Your review still needs to establish whether the material is authoritative, relevant, current enough, and actually supports the answer.

What belongs in the source collection?

Start with sources that can answer your actual question. Say a design studio wants to compare accessibility guidance for a client website. It could collect the relevant official standards and the client’s approved requirements. Adding every article anyone has bookmarked makes retrieval harder to inspect and can mix guidance with opinion.

Give each document a short source record. The record should let another person find the same text and understand why it is included. If a web page changes, keep enough version information to distinguish the passage you reviewed from what a later search happens to return.

• Identity: title, issuing organization or author, and original URL or document reference.
• Scope: the question it can inform, its jurisdiction or audience, and important exclusions.
• Version: publication date, revision date where available, and the version you reviewed.
• Handling: who may read it, whether it can be uploaded to the chosen service, and who maintains it.

Keep permission checks in the retrieval application as well as the source system. Microsoft’s overview recommends controlling access when retrieving documents and treating retrieved text as untrusted input. A passage that tells the assistant to ignore its instructions is source content, not permission to change how the system operates.

How do you know the right evidence was retrieved?

Create test questions before you tune the search. For each question, identify the passage a careful researcher would need. Include an easy direct question, one using different wording, a question that needs two sources, a question with conflicting evidence, and a question the collection cannot answer.

Inspect the retrieved passages without reading the generated answer first. Did the essential passage appear? Were the important qualifications preserved? Did unrelated documents crowd it out? This keeps polished writing from disguising weak retrieval.

Microsoft’s retrieval evaluation guidance↗ (opens in a new tab) describes checking relevance, coverage, and result ordering with labeled test questions. Start with understandable pass or fail notes. More elaborate scores become useful when they help explain a real failure, rather than hiding it behind an average.

What makes a citation trustworthy?

A citation needs to support the claim beside it. Open the original source and find the passage. Check the subject, date, conditions, and exceptions. A source may be genuine and still fail to support the sentence, especially when the assistant combines several passages into a stronger conclusion than any author made.

Ask for a short evidence table during review: claim, source reference, supporting passage location, and any qualification. You do not need to publish the whole table. It is a working aid that makes an unsupported leap easier to find before the answer becomes a client deliverable.

Mark an inference explicitly. “Both documents require review, so we propose one combined checklist” is a proposal grounded in two sources. “Both documents mandate one combined checklist” is a different claim and needs different evidence. The distinction is especially useful when a source describes a possibility rather than a requirement.

Save accepted source references in fenbs Links and listings, with notes explaining what each one supports. Keep the full documents in the approved source system; fenbs is the record of how the team is using them.

What should stay outside the retrieval index?

A retrieval index is a structure that helps search find content. It should not be the only place you keep the research goal, review status, or a person’s approval. Those records change as work progresses, and they need to remain visible even when a particular search does not retrieve them.

Keep a short project record with the question, source-selection rules, evaluation decisions, accepted conclusions, and next action. Link to the retrieval configuration and test evidence rather than copying everything into the record. If a source is withdrawn, record what work needs rechecking as well as updating the collection.

The introduction to RAG covers the underlying technique, while what an AI brain is explains the wider work record. These solve related problems: finding evidence for an answer and keeping the context needed to continue the research responsibly.

How to do this with fenbs

1. Create a free account, then add a project for one research question with its audience and required outcome.
2. Add the approved source references to Links and listings. Record the evidence rules a person chose in Decisions and rules, leaving disputed sources as open questions.
3. Create tasks for retrieval checks, citation review, and unresolved conflicts. Put the method in each task’s plan and record what was tested and what failed.
4. Connect a supported assistant using Integrations. Ask it to read project context before working and save progress, including the next source to check, before stopping.

fenbs does not provide a document retrieval engine. It is not the right fit for storing and searching your research collection; use a system built for that job. Use fenbs when the research work itself needs continuity across people, sessions, or supported assistants.

When is the workflow ready for real research?

Start using it for reviewed work when you can explain its failures as clearly as its successes. A useful test record says which questions it handles, which sources it can access, how citations were checked, and when it must stop. Repeat the relevant tests after changing the documents, retrieval settings, or model.

For ongoing personal work, the AI personal assistant guide discusses a broader scope. Keep this workflow focused on research evidence, and use the context engineering hub for ways to supply and maintain the context around it.