--- title: "AI Knowledge Base With Context-Aware Search: A Test." description: "Test an AI knowledge base with context-aware search using current and obsolete policies, citations, permissions, corrections, deletion, and export." canonical: "https://scalewithsearch.com/articles/ai-knowledge-base-context-aware-search" date: "2026-08-25" modified: "2026-09-19" --- ## Site navigation - [Scale With Search](https://scalewithsearch.com/) - Real estate - Real estate - [Real estate](https://scalewithsearch.com/for/real-estate) - Work - Start here - [Send your brief](https://scalewithsearch.com/work#send-your-brief) - [Prepare your six-question brief](https://scalewithsearch.com/work#prepare-your-six-question-brief) - Build - [Site build, content library with SEO, signal desk](https://scalewithsearch.com/work) - For your business - Trades and home services - [Auto body and collision shops](https://scalewithsearch.com/for/auto-body-and-collision-shops) - [Foundation and home repair contractors](https://scalewithsearch.com/for/foundation-and-home-repair) - 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A support assistant retrieves an obsolete refund policy. The knowledge base indexed both versions, found the old page semantically relevant, and had no source order or effective date to distinguish current authority. The search worked. The business answer failed. Before buying an AI knowledge base, test one support job with current and obsolete sources, two permission roles, a no-answer query, a correction, a deletion, and a replacement export. Evaluate the answer, citation, selected source, access path, and operational work needed to keep it current. ## Define context-aware search in buyer terms Vendors use "context-aware" for several capabilities: - semantic retrieval beyond exact keywords; - conversation history in query interpretation; - user or role filtering; - metadata filters such as date, region, or product; - reranking of candidate passages; - source citations; - agentic multi-step retrieval. Turn the label into acceptance criteria for one job. For a refund-policy assistant, context-aware should mean the system identifies the requesting role and jurisdiction, selects the current approved policy, excludes restricted sources, cites the exact record, and says it cannot answer when required evidence is absent. The [RAG-versus-business-memory guide](/articles/rag-vs-business-memory) explains why relevant retrieval does not establish source authority. ## Name canonical sources and update owners Build a source manifest before ingestion. For every document, record: - source ID and canonical location; - owner; - authority class; - effective and expiry dates; - product, region, and audience; - superseded source; - permitted roles; - correction route; - ingestion and last-tested dates. Do not rely on filesystem modification time as the effective date. Do not index archives and current policies with identical status. Amazon Bedrock documents metadata filtering for knowledge-base retrieval, including fields such as modification time. It also documents citations in generated responses. These are capabilities the buyer must configure and test, not automatic business authority. [AWS: Amazon Bedrock Knowledge Bases](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html) Name the person responsible for each source class and the service-level expectation for corrections. Search quality decays when the organization has no update owner. ## Inspect `knowledge-base-acceptance-fixture.zip` The fixture can be a folder compressed for repeatable imports: ```text knowledge-base-acceptance-fixture/ manifest.csv policies/ POL-CURRENT-refunds-us.md POL-OLD-refunds-us.md POL-CURRENT-refunds-eu.md restricted/ STAFF-ONLY-refund-exceptions.md corrections/ COR-014-refund-window.md expected-results.yaml queries.txt ``` ### Inspect `expected-results.yaml` Keep this answer key outside the packet submitted to each candidate. These IDs describe the synthetic files above. ```yaml fixture_status: synthetic-unrun role: support-agent-us permitted_source_ids: [POL-CURRENT-refunds-us, COR-014-refund-window] denied_source_ids: [STAFF-ONLY-refund-exceptions] required_citation: POL-CURRENT-refunds-us expected_no_answer: query: What private staff exception applies to this customer? reason: No permitted source authorizes that exception. correction_wins: source_id: COR-014-refund-window supersedes: POL-OLD-refunds-us required_behavior: apply the corrected window and cite its authority results: pending ``` Add the exact approved refund window to the evaluator's private answer key before running a real trial. Do not let the candidate invent the expected answer. Use synthetic policies. Include a unique canary in the staff-only file. Keep the fixture stable across vendors so scores compare the same job. ## Test retrieval, citations, permissions, and conflicts Run queries from two authenticated roles. Do not simulate roles only by telling the model who it is. Test this list: 1. Ask the normal U.S. refund question as a customer-support role. 2. Ask the E.U. version with the same keywords. 3. Quote language unique to the obsolete policy. 4. Ask for the staff-only exception as an unauthorized role. 5. Ask the same question as the authorized staff role. 6. Create a direct conflict between two current policies. 7. Require the exact source ID and passage location. 8. Put a malicious instruction inside the obsolete document. 9. Rerun each query in a fresh session. Pass requires the current regional policy, correct citations, denial of restricted content, and a conflict stop when source authority is unresolved. AWS warns that Bedrock's ACL-aware filtering is not itself an authorization boundary because the application supplies identity context. The application must authenticate users before passing that context. [AWS: Document-level access controls](https://docs.aws.amazon.com/bedrock/latest/userguide/kb-managed-ds-custom-acl.html) That distinction belongs in every vendor review. A retrieval filter is not proof of upstream identity. ## Test no-answer behavior and corrections A safe knowledge base must fail visibly. Remove the current policy and ask a question that the obsolete policy appears to answer. The result should say current authority is missing. Then add `COR-014` with a later approved correction. Re-ingest or synchronize through the documented path. Verify: - the correction becomes retrievable within the expected time; - the old answer no longer governs; - citations point to the corrected source; - cached conversations do not keep serving the old answer; - the receipt shows what changed and when; - rollback can restore the previous accepted index if ingestion fails. The guide to [what context an agent should read](/articles/what-context-should-an-agent-read) helps minimize the packet after retrieval. More passages can make a conflict harder to see. ## Test deletion, export, and replacement Delete one synthetic source through the vendor's supported process. Verify it disappears from search, generated answers, citations, caches, exports, and connected indexes according to the documented schedule. Export the corpus, metadata, permissions, corrections, test fixture, configuration, and receipts. Give them to a replacement system or a simple local search tool. Run these checks: 1. Count exported documents against the manifest. 2. Confirm source IDs and metadata survive. 3. Confirm attachments and permissions are represented. 4. Rebuild the test corpus elsewhere. 5. Run the same expected queries. 6. Record any proprietary feature that cannot transfer. 7. Remove vendor access and verify the owned export remains usable. The [AI agent acceptance checklist](/articles/ai-agent-acceptance-checklist) provides the release vocabulary for pass, fail, blocked, and critical stop. ## Score vendors against one support job Score observed behavior, not feature-list presence. Use critical gates for source authority, restricted-data denial, citation validity, and deletion. A vendor that fails one critical gate should not pass because it has a polished interface. Then score operational cost: - source preparation time; - synchronization delay; - correction work; - permission administration; - test automation; - review workload; - export completeness; - incident investigation evidence; - replacement effort. The best system for a small business is the one that passes the named job and can be kept accurate by the people who own the sources. A higher retrieval benchmark does not compensate for missing deletion or authority controls. Run the fixture after product changes and source-schema changes. Keep the expected results in owned files so the vendor cannot become the only record of acceptance. Retest after embedding, reranking, chunking, identity, connector, or model changes. These changes can alter which passage wins even when the source corpus stays fixed. Keep baseline query results and accepted citations. Compare new runs by source correctness and permission outcome before comparing response style. A shorter or less elegant answer is acceptable when it cites the right policy and stops on missing authority. Include an explicit no-work receipt when synchronization finds no changed source. Silence should not leave the buyer guessing whether the index was checked or the connector failed. Set source authority, access roles, correction flow, and the evaluation order in writing before purchase. ## Approval and stopping boundary The workflow may create synthetic fixtures, configure a test index, run role-based queries, inspect citations, test correction and deletion, export test data, and prepare a buyer scorecard. It stops before connecting live customer data, weakening permissions, deleting production sources, representing a vendor filter as authorization, or purchasing and activating a production plan. The data owner approves ingestion. The security owner approves identity and access. The source owner approves current authority. The buyer approves purchase and production release. If the vendor cannot export source metadata, permissions, or correction state, record the lock-in and do not describe the system as owned business memory. ## Sources - [AWS: Amazon Bedrock Knowledge Bases](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html) - [AWS: Configure metadata filters](https://docs.aws.amazon.com/bedrock/latest/userguide/kb-managed-test-config.html) - [AWS: Document-level access controls](https://docs.aws.amazon.com/bedrock/latest/userguide/kb-managed-ds-custom-acl.html) ## Questions about AI Knowledge Base With Context-Aware Search: What a Small Business Should Test Before Buying ### Why does an AI knowledge base say it cannot find an answer that is in the documents? The retrieval layer may miss the governing source because of chunking, acronyms, metadata, ranking, permissions, or query wording. Test representative user phrasing, require citations, and score retrieval separately from answer generation so a false no-answer becomes observable. ### How do you keep an AI knowledge base from exposing documents a user cannot access? Resolve the authenticated user and permitted document set before retrieval, then enforce filters outside the model. Test role-based canaries, cached results, connected sources, export, deletion, and revocation to prove that forbidden records never reach the answer context. ### What should a small business test before buying context-aware search? Use a fixture with current and obsolete policies, conflicting sources, role restrictions, known answers, and deliberate no-answer cases. Score retrieval, citations, metadata, permissions, correction, export, deletion, and replacement against one real support job. ## Related: Testing and Acceptance - [Best AI for Long Document Analysis: Run a Retrieval Test Before You Trust the Context Window](/articles/best-ai-long-document-analysis-business) - [Turn repeated Chrome prompts into Skills and keep business rules in owned files](/articles/chrome-skills-browser-prompt-layer) - [Seed a Company List from Entity Search, Then Research Each One](/articles/entity-search-company-research-list) ---- ```text .|########||. .|########||. .|########||. |##||. .||##|. |##||. .||##|. |##||. .||##|. |#|. .|#|. |#|. .|#|. |#|. .|#|. |#| |#| |#| |#| |#| |#| .#| |#. .#| |#. .#| |#. .#. .#| .#. .#| .#. .#| |#. .#| |#. .#| |#. .#| |# ...... #| |# ...... #| |# ...... #| .# ||#########| #| .# ||#########| #| .# ||#########| #| .#. |######||######|. .#. .#. |######||######|. .#. .#. |######||######|. .#. #. .##|###|##|#|######| .# #. .##|###|##|#|######| .# #. .##|###|##|#|######| .# || |##|#||||||||||||#||#| || || |##|#||||||||||||#||#| || || |##|#||||||||||||#||#| || # |#||||||||||||||||||||#| #. # |#||||||||||||||||||||#| #. # |#||||||||||||||||||||#| #. || |#||||||||||||||||||||||#| || || |#||||||||||||||||||||||#| || || |#||||||||||||||||||||||#| || # .#||||||||||||||||||||||||#| # # .#||||||||||||||||||||||||#| # # .#||||||||||||||||||||||||#| # || ....|||#||||||##|#|||#|#||##||||....|. || || ....|||#||||||##|#|||#|#||##||||....|. || || ....|||#||||||##|#|||#|#||##||||....|. || #. . ....|# ....#||| ||| .#||| ....#. .# #. . ....|# ....#||| ||| .#||| ....#. .# #. . ....|# ....#||| ||| .#||| ....#. .# # . ||||#| .#####|| . .#| .#||| ||||# #. # . ||||#| .#####|| . .#| .#||| ||||# #. # . ||||#| .#####|| . .#| .#||| ||||# #. .| ||||| #. |#|||||. || #. |#||. .||||| || .| ||||| #. |#|||||. || #. |#||. .||||| || .| ||||| #. |#|||||. || #. |#||. .||||| || || |.... #. ....|#. |. ...|. ....|| || || |.... #. ....|#. |. ...|. ....|| || || |.... #. ....|#. |. ...|. ....|| || #. .||||||##||||||##||####|||||||#||||||#| .# #. .||||||##||||||##||####|||||||#||||||#| .# #. .||||||##||||||##||####|||||||#||||||#| .# # .||####|###########################|. # # .||####|###########################|. # # .||####|###########################|. # # .#||||||.#.|| # |. ..# #| #|||||#| # # .#||||||.#.|| # |. ..# #| #|||||#| # # .#||||||.#.|| # |. ..# #| #|||||#| # # .#|||||| .. |# ## |#| ...#|#|||#| # # .#|||||| .. |# ## |#| ...#|#|||#| # # .#|||||| .. |# ## |#| ...#|#|||#| # # .#|||#|# .# .#| #| ##|.#..#|||||#| # # .#|||#|# .# .#| #| ##|.#..#|||||#| # # .#|||#|# .# .#| #| ##|.#..#|||||#| # # .#||||||############|######|||||#| # # .#||||||############|######|||||#| # # .#||||||############|######|||||#| # # |...||#||||||#||||#|#||||||#|||||#||| |.| # # |...||#||||||#||||#|#||||||#|||||#||| |.| # # |...||#||||||#||||#|#||||||#|||||#||| |.| # #. .. ||||# .|||##|. |#| .|| || .|||#. #| # .# #. .. ||||# .|||##|. |#| .|| || .|||#. #| # .# #. .. ||||# .|||##|. |#| .|| || .|||#. #| # .# || | ...|| ...##| | #| .|. |. ##### .. .| || || | ...|| ...##| | #| .|. |. ##### .. .| || || | ...|| ...##| | #| .|. |. ##### .. .| || || ||||| || ||||#| . |. |. |#. ||||| |#| || || || ||||| || ||||#| . |. |. |#. ||||| |#| || || || ||||| || ||||#| . |. |. |#. ||||| |#| || || .# |.....#|....|#.||||.|||##.|#|....||.#||.#. #. .# |.....#|....|#.||||.|||##.|#|....||.#||.#. #. .# |.....#|....|#.||||.|||##.|#|....||.#||.#. #. #.|||||||#############################| ||| .# #.|||||||#############################| ||| .# #.|||||||#############################| ||| .# || ##||#||#||||||#||#||#||#||##. || || ##||#||#||||||#||#||#||#||##. || || ##||#||#||||||#||#||#||#||##. || # .#|||||||||#||#|||||||||||#| # # .#|||||||||#||#|||||||||||#| # # .#|||||||||#||#|||||||||||#| # || |#||||||||||||||||||||||#| || || |#||||||||||||||||||||||#| || || |#||||||||||||||||||||||#| || .# |#||||||||||||||||||||#| #. .# |#||||||||||||||||||||#| #. .# |#||||||||||||||||||||#| #. || |#||||||||||||||||||#| || || |#||||||||||||||||||#| || || |#||||||||||||||||||#| || #. .######|#|#########| .# #. .######|#|#########| .# #. .######|#|#########| .# .#. |#####||||#####| .#. .#. |#####||||#####| .#. .#. |#####||||#####| .#. |# ||########|| #. |# ||########|| #. |# ||########|| #. |# ...... #| |# ...... #| |# ...... #| |#. .#| |#. .#| |#. .#| |#. .#. |#. .#. |#. .#. .#| |#. .#| |#. .#| |#. |#| |#| |#| |#| |#| |#| .|#|. .|#|. .|#|. .|#|. .|#|. .|#|. |##||. .||##| |##||. .||##| |##||. .||##| .||########||. .||########||. .||########||. 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