SEPTEMBER 2023 RELEASES
September 2023 Releases
RAG Hygiene: How to Scale and Maintain AI Agent Knowledge
Retrieval-Augmented Generation (RAG) gives AI agents access to external knowledge in real time, allowing them to respond with more factual, contextually relevant answers without needing to embed all content in the prompt.
It separates how the agent behaves from knowledge it can access conditionally based on the conversational context.
But as powerful as RAG is, it’s only as good as the material it pulls from.
Poorly structured documents, stale information, and content duplication all reduce retrieval quality and increase the risk of hallucinations or vague answers. A clean, well-maintained RAG setup is essential for reliability, speed, and scalability.
Content structure, size, and recency directly impact the quality of the chunks that are retrieved.
And because retrieval is conditional (only triggered when the context calls for it), retrieval failures are often silent, which makes proactive hygiene even more important.
What Makes Up a RAG System?
While exact implementations vary, most RAG systems (including Regal’s) are built around three key components:
1. Source Documents
Uploaded files or public URLs that contain the knowledge agents should reference. Regal supports formats like PDF, DOCX, Markdown, and plaintext, as well as up to 300 URLs per knowledge base.
2. Chunking and Indexing
Documents are automatically split into semantically meaningful segments (“chunks”), embedded into a vector index, and retrieved dynamically based on live conversation context.
3. Contextual Retrieval
When the agent receives a question, the system evaluates the current state of the conversation and determines whether it needs to retrieve relevant information from the KB. If so, top-matching chunks are surfaced to the model in real time.
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In platforms like Regal, knowledge bases are attached at the agent level, and URL-based sources can be refreshed periodically to reflect the latest content without re-uploading.
Best Practices for Maintaining a Hygienic RAG Setup
A well-maintained RAG system isn’t just about uploading documents. It's about curating, formatting, and revisiting your knowledge over time so the agent retrieves the right information, every time. Here's how to do it right.
1. Use Clean, Structured Source Material
Start with source documents that are well-formatted and easy to chunk. Use descriptive headings, clear paragraphs, and bullet points to signal boundaries. Unstructured walls of text make retrieval noisy and reduce the model’s ability to isolate relevant information.
Avoid including spreadsheets or tables inside the KB as RAG can’t reliably extract structured data. Use a custom action instead if the content looks like a CSV.
2. Break Long Docs Into Logical Units
Instead of uploading a massive multi-topic PDF or wiki page, break content into smaller, focused documents. Each should cover a single topic (e.g. "California Refund Policy" or "Device Setup – Model X").
This helps ensure chunk boundaries are semantically meaningful and retrievals stay on-topic. Overly long documents result in oversized or diluted chunks, which confuse the agent and waste context space.
3. Title for Clarity and Retrieval
Titles aren’t cosmetic—they’re indexed, and influence which content gets retrieved. The description matters.
A vague title like “FAQ_v7b” gives no signal to the model. A title like “Silver Plan Eligibility Requirements – 2024” improves match quality and document traceability.
Think of titles as metadata. Be precise, consistent, and scoped. Be specific on when the agent should reference it. If done right, you won't need to prompt the agent to retrieve info, it'll infer how to do so on its own.
4. Avoid Redundancy and Version Bloat
Uploading similar versions of the same content (like three refund policies with minor edits) will reduce retrieval precision and increase the chance of conflicting answers. RAG doesn’t know which one is “correct.”
Instead:
- Replace outdated versions when uploading new ones.
- Consolidate overlapping documents where possible.
- Keep a single, authoritative version per topic.
5. Document the Purpose of Each KB
Each Knowledge Base requires a description that provides execution context to the LLM. This field determines when the model should query the KB and how to handle retrieval.
Use the description to specify scope and any conditional logic, like “Use this KB only when the user asks about pricing for existing customers.”
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And again, be specific, and keep clear pathways. Keep thematically different information in separate Knowledge Bases, and thematically similar information in the same Knowledge Base. This way, there’s no noise introduced to how the LLM handles retrieval.
Do not include: Metadata such as ownership, versioning, or last updated timestamps. The description is evaluated at runtime and should remain focused only on retrieval behavior.
6. Keep Content Fresh With Scheduled Reviews
Stale content is one of the most common causes of poor retrievals. Product specs, benefits, and policy language change frequently. If you don’t update the KB, the agent won’t know what’s current.
In Regal:
- Public URLs can be rescraped on a custom cadence.
- Static files like PDFs and DOCX need to be manually replaced.
- Create a monthly or quarterly review cycle across owners (product, legal, CX).
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7. Test Retrieval Behavior in Context
Just because a document is uploaded doesn’t mean it’s working. Use real queries to test retrieval behavior in staging or preview environments. Look at which chunks are being returned, how relevant they are, and what the agent is doing with them.
Ask yourself:
- Is the right information being pulled?
- Is it current?
- Is it too broad or off-topic?
- Is it failing to retrieve at all?
8. Prune Often
As your KB grows, so does the risk of drift. More documents mean more retrieval noise, especially if some are outdated or poorly scoped. Periodically remove unused, redundant, or irrelevant documents.
In RAG, quality matters more than quantity. A smaller, well-maintained KB often outperforms a larger, unstructured one.
RAG Done Right: Ensuring Accurate, Reliable Retrieval
RAG is a critical capability for modern AI agents, but only when paired with disciplined content management. Clean structure, scoped inputs, and regular updates ensure your retrieval system surfaces the right knowledge at the right time, without guesswork or degradation.
Whether you're using Regal or another platform, the principles are the same: format clearly, avoid duplication, label precisely, and update regularly. A well-maintained knowledge base keeps your agent sharp and makes your system easier to debug, scale, and trust.
Want to see how your current KB setup stacks up? Schedule a demo now.
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