You just updated your company’s “Remote Work Policy” PDF. You pushed it to the vector database. You feel good.
Five minutes later, an employee asks the HR Agent: “Can I work from Hawaii?”
The Agent replies: “Yes, but only for 2 weeks, and also No, because the policy is strictly hybrid.”
It hallucinated. It mixed the old policy (chunk 452) with the new policy (chunk 890). It created a frankenstein answer that is legally dangerous and factually wrong.
And you had no idea until the screenshot hit Slack.
The “Silent Failure” of RAG
We treat Retrieval-Augmented Generation (RAG) like magic. We assume that if we shove documents into a vector store (Pinecone, Weaviate), the model will “figure it out.”
But RAG systems don’t crash. They don’t throw 500 Internal Server Error when they retrieve the wrong context. They just confidently lie.
In 2026, Context Rot is the new Technical Debt.
Every time you update a document, change a chunking strategy, or swap an embedding model, you are introducing risk. You are changing the brain of your agent.
Yet, we test this changes with “Vibe Checks.” We ask one question, it looks okay, and we deploy.
Code vs. Knowledge
If a developer changed 500 lines of code in the billing engine, you wouldn’t just “glance at it” and merge. You would run a regression suite. You would check for side effects.
But when a Knowledge Engineer changes 500 paragraphs of policy, we merge it blindly.
Why? Because we lack the infrastructure.
You can’t “unit test” a PDF. You can’t run a linter on a Notion page. The only way to verify knowledge is to query it.
The Behavior Diff
To fix RAG, we need to stop looking at File Diffs and start looking at Behavior Diffs.
- File Diff:
policy_v1.pdfvspolicy_v2.pdf(Useless to a human). - Behavior Diff:
- Question: “Can I work from Hawaii?”
- Before: “Yes, for 2 weeks.”
- After: “No, remote work is restricted.”
To see this diff, you need a runtime. You need an environment where the new data exists, but the old data is still the baseline.
The Ephemeral Vector Store
This is where PrevHQ enters the stack.
We are seeing a new pattern emerge among top AI teams: The Ephemeral Knowledge Sandbox.
When you open a PR to update a document (or the RAG code), PrevHQ spins up a completely isolated environment.
- Fresh Infrastructure: A dedicated vector index is created.
- Ingestion: The new documents are ingested.
- Simulation: We run a gauntlet of 50 “Golden Questions” (Evals) against this specific sandbox.
We don’t just check if the answer changed. We check if it changed correctly.
The Librarian is now an Engineer
The role of the “Knowledge Manager” has changed. You are no longer just organizing files. You are an Engineer responsible for the reliability of the company’s intelligence.
If you control the Context, you control the Agent.
Don’t let your Knowledge Base become a liability. Treat your data updates like code updates. Test them in a sandbox, verify the behavior, and only then let them touch the truth.
FAQ: How to Test RAG Pipelines
Q: How do I measure RAG accuracy automatically?
A: LLM-as-a-Judge. You cannot check exact string matches (because LLMs phrase things differently every time). Instead, you use a stronger model (e.g., GPT-5) to grade the answer. “Does Answer A contain the same facts as the Golden Answer?” You run this evaluation inside your PrevHQ sandbox for every PR.
Q: What is “Chunking Strategy” and why does it break things?
A: It’s how you slice the data. If you slice a document into 500-token chunks, you might cut a sentence in half. If you slice it into 1000-token chunks, you might dilute the meaning. Changing this setting is a major breaking change. You must test it against your entire question set to ensure retrieval didn’t degrade.
Q: Can I just test in Staging?
A: No. Staging data is stale. If you are testing a new document, you need an environment where that document is indexed and active right now. A persistent staging server might have conflicts or old embeddings. An ephemeral sandbox ensures a clean slate for every test.
Q: How many questions should be in my “Golden Set”?
A: Start with 50. Focus on the “High Risk” questions (Refunds, Compliance, Safety). These are the questions where a hallucination costs money. As you find bugs, add them to the set as regression tests.