According to research across enterprise deployments, 72% of enterprise RAG implementations fail in the first year. The failure isn’t in the retrieval pipeline. It’s in what goes into it.
This guide covers a 7-step framework for building a production-ready enterprise LLM knowledge base. It starts with a mandatory data governance audit before any embedding work begins, then moves through architecture selection, source connection, embedding, vector storage, retrieval wiring, and ongoing freshness monitoring. Expect 6–12 weeks from Step 0 to a production system.
What you need before you start:
- A named data owner for each source system you plan to connect
- Stakeholder sign-off on what data the LLM can surface, and to whom
- A preliminary sensitivity classification policy (public / internal / confidential / restricted)
- Admin or read access to all source systems (Confluence, SharePoint, Notion, databases)
- An embedding model API key or local endpoint (OpenAI
text-embedding-3-large, Cohereembed-v3, orBAAI/bge-m3via sentence-transformers) - A vector database provisioned (Pinecone, Weaviate, Chroma, or pgvector on Postgres)
- An orchestration library installed (
langchain>=0.2orllama-index>=0.10)
Who this guide is for: Data engineers and AI/ML teams in enterprises building their first production knowledge base, or teams already in pilot and hitting quality walls.
Below, we cover: why most builds fail before retrieval, the 7 steps to build one that doesn’t, common pitfalls, and how Atlan accelerates the governance work.
Why most LLM knowledge base builds fail before retrieval
Permalink to “Why most LLM knowledge base builds fail before retrieval”Most enterprise LLM knowledge base failures happen upstream of the pipeline. Research on enterprise RAG deployments finds the dominant failure mode is not retrieval architecture or model selection; it’s source data quality.
In most enterprises, the same document exists in 3–5 versions across SharePoint, email archives, and local drives. RAG retrieves whichever version is semantically closest to the query, not the most current. The result is a system that answers confidently from a deprecated policy, a superseded product spec, or a document written by someone who left the company two years ago.
The downstream cost is real. A 2025 study restricted a RAG system to high-quality curated content and measured hallucinations at near zero. The same system fed unvetted baseline data fabricated responses for 52% of out-of-scope questions. That’s not a retrieval problem. It’s a source data problem. And no existing “how to build” guide addresses it. That is why the failure rate holds steady at 72%.
This guide starts at Step 0: the governance audit that every other guide skips. If you’ve already read about what an LLM knowledge base actually is and understand why LLM hallucinations happen, you’re ready to build one that works.
Step 0: Audit your source data before embedding anything
Permalink to “Step 0: Audit your source data before embedding anything”Time required: 1–3 weeks (longer if no existing governance metadata exists)
This is the step every existing guide skips. It’s also the step that explains why 72% of enterprise RAG implementations fail. Skipping Step 0 doesn’t just degrade retrieval quality. It creates liability: a knowledge base that surfaces confidential financial projections to anyone who can type a question.
What you’ll accomplish: A curated, certified document inventory. Every source is flagged with currency status, sensitivity tier, ownership, and AI-use authorization before a single token touches an embedding model.
1. Inventory your candidate sources
Permalink to “1. Inventory your candidate sources”List every system whose documents you plan to ingest: Confluence spaces, SharePoint libraries, Notion workspaces, database exports, PDF repositories. For each source, document: who owns it, when it was last updated, and what sensitivity level applies.
Before building this list from scratch, check whether your data catalog for AI already has this metadata. If your organization uses Atlan, dbt Cloud, or Alation, ownership, lineage, and classification may already exist. That means Step 0 compresses significantly rather than requiring a full build from scratch.
2. Classify sensitivity before ingestion
Permalink to “2. Classify sensitivity before ingestion”Apply a four-tier model: public / internal / confidential / restricted. Documents tagged confidential or restricted require explicit approval before indexing, or must be excluded from the knowledge base entirely. This classification becomes the access control layer in Step 5.
3. Certify documents for AI use
Permalink to “3. Certify documents for AI use”Not every current document is AI-ready. Draft documents, deprecated policies, and materials authored by former employees should be flagged or excluded. Create a certified_for_ai: true/false tag in your document system. Only documents with a positive certification should proceed to ingestion.
4. Map to a single system of record
Permalink to “4. Map to a single system of record”For documents that exist in multiple versions, designate one authoritative source. Only ingest from that source. Governance frameworks for RAG consistently identify version proliferation as the most common and most fixable root cause of retrieval failures.
5. Set freshness thresholds
Permalink to “5. Set freshness thresholds”Decide how stale is too stale: 30 days for policies? 90 days for product docs? Document these thresholds now. You will need them in Step 7 when you wire freshness monitoring.
Validation checklist:
- [ ] Every candidate source has a named owner
- [ ] All documents have a sensitivity tier assigned
- [ ] Version-of-record is designated for any document with duplicates
- [ ]
certified_for_aitag or equivalent applied to all candidate documents - [ ] Freshness thresholds documented per source type
Common mistakes:
“Let’s ingest everything and filter later” is the most costly mistake teams make. Retrieval filters cannot compensate for ungoverned source data. The LLM will surface whatever is semantically closest, not whatever is trustworthy. Treat pre-ingest certification as a hard gate, not a soft recommendation. Also, do not assume your SharePoint permissions model transfers to the vector store automatically. Explicitly map access control rules to retrieval filtering logic before you build the pipeline.
For a detailed treatment of the preparation work that makes Step 0 tractable, read the companion guide on knowledge base data preparation for LLMs.
Step 1: Define scope and use case
Permalink to “Step 1: Define scope and use case”Time required: 3–5 days
An unbounded knowledge base is ungovernable. Scoping to a specific domain — internal HR policy Q&A, product documentation for support agents, a financial analyst research corpus — lets you govern Step 0 rigorously and evaluate retrieval quality against a known ground truth.
1. Define the primary audience and query patterns
Permalink to “1. Define the primary audience and query patterns”Support agents asking product questions behave differently from analysts interrogating data dictionaries. The query type shapes chunking strategy and retrieval design later. Write down: who is asking, what kinds of questions they ask, and how they phrase them.
2. Set a domain boundary
Permalink to “2. Set a domain boundary”What is explicitly in scope? What is explicitly out of scope? Document this formally and get stakeholder sign-off. Out-of-scope queries should return “I don’t have enough information to answer this” — not a hallucinated answer from an adjacent document. Leaving scope undefined is one of the fastest routes to the failure mode described in the opening.
3. Write 20–30 gold-standard Q&A pairs
Permalink to “3. Write 20–30 gold-standard Q&A pairs”These become your evaluation set in Step 6. If you cannot write 20 questions the knowledge base should answer correctly, the scope is not well-defined. Each pair should require at least one specific, verifiable fact from the source corpus — prefer “Under what conditions does policy X require VP approval?” over “What is our policy on X?” Build the evaluation set before you build the pipeline. It forces scope clarity and gives you a measurable quality signal before any stakeholder demo.
Validation checklist:
- [ ] Primary audience documented
- [ ] Domain boundary agreed with stakeholders
- [ ] 20+ gold-standard Q&A evaluation pairs written
Build your AI context stack
Get the Stack GuideStep 2: Connect governed sources
Permalink to “Step 2: Connect governed sources”Time required: 1–2 weeks
The difference between an enterprise knowledge base and a demo is metadata preservation. When retrieval returns a chunk, your system should be able to answer three questions: where did this come from, who owns it, and is it still current? Raw document dumps cannot answer any of them.
1. Use loaders that preserve source metadata
Permalink to “1. Use loaders that preserve source metadata”LangChain’s ConfluenceLoader, SharePointLoader, and NotionDBLoader return Document objects with metadata dicts. Extend these to include sensitivity_tier, certified_for_ai, owner, and last_modified. Every Document object should carry: source_url, owner, sensitivity_tier, certified_for_ai: true, last_modified, ingestion_timestamp.
2. Tag every document at ingestion time
Permalink to “2. Tag every document at ingestion time”The metadata tags you assigned in Step 0 must travel with the document through the entire pipeline. Don’t strip metadata for convenience at the ingestion layer — you’ll need it for namespace isolation in Step 5 and access filtering in Step 6. Enterprise RAG ingestion at scale consistently treats metadata preservation as a non-negotiable requirement.
3. Build a refresh schedule
Permalink to “3. Build a refresh schedule”New or updated documents in source systems won’t appear in the knowledge base until reingested. Set up incremental ingestion keyed on last_modified timestamps. Choose daily refresh for high-velocity sources — wikis and product documentation updated more than once a week. Weekly refresh is sufficient for stable sources like HR policies or compliance frameworks updated quarterly. This feeds directly into the freshness monitoring job in Step 7. For deeper coverage of data preparation strategies, the companion guide on knowledge base data preparation for LLMs covers preparation at the source level.
Validation checklist:
- [ ] All documents carry full metadata at ingestion
- [ ] Incremental refresh schedule configured
- [ ] Restricted/confidential documents either excluded or behind access filter
Step 3: Choose your architecture: RAG, wiki, or hybrid
Permalink to “Step 3: Choose your architecture: RAG, wiki, or hybrid”Time required: 3–5 days (decision + proof of concept)
The architecture choice matters less than most guides suggest — but it matters. And the April 2026 Karpathy LLM wiki architecture, which routes knowledge as human-readable text rather than embedding vectors, has reopened the RAG-vs-wiki debate with real momentum. The short answer: the governance problem from Step 0 applies to both approaches equally. A stale wiki is as unreliable as a stale vector store.
1. RAG for most enterprise use cases
Permalink to “1. RAG for most enterprise use cases”Semantic retrieval over a governed document corpus works well for unstructured content: policies, runbooks, product docs, support knowledge. Start here. Retrieval-augmented generation lets you keep knowledge external to the model, which means it can be updated without retraining. Use LangChain or LlamaIndex for orchestration. For a direct comparison of the two main options, fine-tuning vs. RAG clarifies when each approach applies.
2. GraphRAG for relationship-rich data
Permalink to “2. GraphRAG for relationship-rich data”If your knowledge base needs to answer questions about how entities relate (org charts, data lineage, product hierarchies, regulatory dependency trees), GraphRAG surfaces relationships that chunk-based RAG misses. Enterprise GraphRAG architecture demands careful ontology and taxonomy management. That is another area where a data catalog provides native structure, not a reason to delay.
3. Fine-tuning is rarely the right answer for knowledge bases
Permalink to “3. Fine-tuning is rarely the right answer for knowledge bases”Fine-tuning encodes knowledge into model weights at a point in time. It cannot be updated without retraining. It is expensive to maintain. Reserve fine-tuning for style and task adaptation, not factual recall. If your source data changes (and it will), a fine-tuned model will silently serve stale answers with high confidence.
Validation checklist:
- [ ] Architecture decision documented and justified for your use case
- [ ] Proof of concept run against a 50–200 document sample corpus. Retrieval returns correct results for at least 3 of 5 test queries drawn from the Step 1 evaluation set
- [ ] Governance argument from Step 0 confirmed to apply to chosen architecture (staleness, sensitivity, certification requirements hold regardless of RAG vs. wiki vs. GraphRAG)
Inside Atlan AI Labs: how governed metadata drives 5x better AI accuracy
Download E-BookStep 4: Generate metadata-aware embeddings
Permalink to “Step 4: Generate metadata-aware embeddings”Time required: 1–2 weeks
Pure semantic embeddings lose the metadata you carefully attached in Step 2. Retrieval that cannot filter by sensitivity_tier or certified_for_ai will surface ungoverned content. The embedding layer is where governance context must survive the transition from document to vector.
1. Choose a chunking strategy matched to your content
Permalink to “1. Choose a chunking strategy matched to your content”Fixed-size chunking (chunk_size=512, overlap=50) works for dense technical documentation. Semantic chunking (splitting on topic boundaries rather than token count) performs better for long-form narrative content. Hierarchical chunking preserves parent-child document structure for better context when answering questions that span subsections. Default chunk sizes in LangChain and LlamaIndex are tuned for demos, not for enterprise document diversity. Test on a sample corpus before committing.
2. Select an embedding model
Permalink to “2. Select an embedding model”OpenAI text-embedding-3-large is the strongest general-purpose embedding model as of 2026. For on-premise or privacy-constrained deployments, BAAI/bge-m3 via sentence-transformers is a strong open-source alternative. Understanding what embeddings are and how they work helps you evaluate trade-offs between model performance, latency, and cost.
3. Preserve metadata through embedding
Permalink to “3. Preserve metadata through embedding”Every vector stored in the vector database should carry its full metadata payload: source_url, sensitivity_tier, owner, last_modified, certified_for_ai. Do not embed content-only and discard the governance context. The metadata payload is what makes your vector store a governed knowledge base rather than a generic semantic index.
Validation checklist:
- [ ] Chunk size and overlap documented and justified for content type
- [ ] Embedding model selected and access confirmed
- [ ] Every vector carries full metadata payload
- [ ] Sample corpus embedded and inspected. Metadata survives embedding
Step 5: Build your vector store
Permalink to “Step 5: Build your vector store”Time required: 1 week
The vector database is not just a storage layer. It is the access control enforcement point. Metadata filters applied at query time are your primary defense against surfacing restricted content to users who should not see it. This is the step where the sensitivity classification work from Step 0 pays off directly.
1. Choose a vector database matched to your scale and stack
Permalink to “1. Choose a vector database matched to your scale and stack”Pinecone (managed, serverless) for teams that want zero infrastructure overhead. Weaviate or Chroma for self-hosted control and open-source auditability. pgvector for teams already on Postgres who want to minimize new infrastructure. What a vector database actually does and how it differs from a traditional search index matters for this decision, particularly around approximate nearest neighbor (ANN) index configuration and filtering behavior.
2. Isolate namespaces by sensitivity tier
Permalink to “2. Isolate namespaces by sensitivity tier”Store public and internal documents in separate namespaces or collections. Never mix sensitivity tiers in the same namespace. A misconfigured filter query against a mixed namespace would expose restricted content, and that failure mode is silent. The system returns results without surfacing the access violation.
3. Configure metadata filters as query defaults
Permalink to “3. Configure metadata filters as query defaults”At retrieval time, always filter on certified_for_ai: true as a baseline. Add sensitivity_tier filtering keyed to the requesting user’s access level. Treat these filters as defaults that must be explicitly overridden for testing, not as optional additions.
Validation checklist:
- [ ] Vector database provisioned and accessible
- [ ] Namespaces isolated by sensitivity tier
- [ ] Default metadata filter (
certified_for_ai: true) applied to all query paths - [ ] Test query against restricted namespace confirms no cross-contamination
Step 6: Wire retrieval and LLM response
Permalink to “Step 6: Wire retrieval and LLM response”Time required: 1–2 weeks
An end-to-end retrieval chain connects query to vector search to context assembly to LLM response. The implementation is straightforward in LangChain or LlamaIndex. The quality gate (evaluation against the gold-standard pairs you wrote in Step 1) is where most teams underinvest.
1. Build the retrieval chain
Permalink to “1. Build the retrieval chain”In LangChain: RetrievalQA.from_chain_type(llm=llm, retriever=vectorstore.as_retriever(search_kwargs={"k": 5, "filter": {...}})). In LlamaIndex: QueryEngine with metadata filters pre-applied. The filter passed into the retriever should always include the governance defaults from Step 5. See the full reference on retrieval-augmented generation for orchestration patterns.
2. Write a grounding prompt template
Permalink to “2. Write a grounding prompt template”Explicitly instruct the model: “Answer only from the context provided. If the context does not contain sufficient information to answer this question, respond: ‘I don’t have enough information to answer this reliably.’” This grounding instruction is your primary hallucination guard. It is not foolproof, but it dramatically reduces the surface area for confabulation.
3. Run evaluation against gold-standard pairs
Permalink to “3. Run evaluation against gold-standard pairs”RAG evaluation in 2026 centers on two metrics: faithfulness (does the answer contradict the source?) and relevance (did retrieval surface the right chunks?). Target faithfulness above 0.9 before moving to production. Track the percentage of queries returning “I don’t know.” Too high means a coverage gap; too low may indicate hallucination.
Validation checklist:
- [ ] End-to-end query returns grounded response with source citation
- [ ] Faithfulness score above 0.9 on gold-standard evaluation set
- [ ] Out-of-scope queries return “I don’t know” rather than hallucinated responses
- [ ] Retrieval returns source metadata so the user can verify provenance
Step 7: Monitor freshness, certification, and quality
Permalink to “Step 7: Monitor freshness, certification, and quality”Time required: 1 week to wire; ongoing operational
A knowledge base without freshness monitoring is a knowledge base that is silently wrong. Enterprise documents change faster than most teams assume. Policies, product specs, and compliance frameworks are routinely revised on quarterly or annual cycles. Without active monitoring, your certified knowledge base becomes ungoverned again within one fiscal quarter: exactly the state you spent Step 0 correcting.
1. Set up a freshness check job
Permalink to “1. Set up a freshness check job”Daily or weekly: query each source system for documents updated since last ingestion. Flag documents that have changed as needs_recertification. Pull them from retrieval (remove the certified_for_ai: true flag) until the document owner has reviewed and reapproved. Active metadata management platforms can automate this trigger-and-notify workflow, eliminating the manual freshness check job entirely.
2. Wire re-certification triggers
Permalink to “2. Wire re-certification triggers”When a source document is updated, automatically notify the document owner for re-review. Only reinstate the certified_for_ai: true flag after owner sign-off. This closes the loop between source document change and knowledge base update, and prevents version proliferation failures from silently reappearing post-launch.
3. Monitor retrieval quality metrics
Permalink to “3. Monitor retrieval quality metrics”Track faithfulness score over time, the percentage of queries returning “I don’t know” (coverage signal), and user thumbs-down feedback rate. Build a quality dashboard before go-live. If faithfulness drops or the “I don’t know” rate climbs, your source data has likely drifted. Freshness monitoring surfaces the specific documents responsible.
Validation checklist:
- [ ] Freshness check job running on schedule
- [ ] Stale documents automatically paused from retrieval
- [ ] Owner notification triggered on source document update
- [ ] Quality dashboard with faithfulness and coverage metrics live
Common implementation pitfalls
Permalink to “Common implementation pitfalls”Even well-resourced teams hit the same five failure modes repeatedly. Enterprise LLM deployment research identifies poor data quality and siloed data as the top contributors to failed knowledge base projects, ahead of model selection, retrieval architecture, and latency issues.
Pitfall 1: Ingesting everything without a pre-ingest gate
Permalink to “Pitfall 1: Ingesting everything without a pre-ingest gate”Teams want to move fast. Governance feels like a speed bump. Source data quality is the dominant driver of the 72% enterprise RAG failure rate, and Step 0 is specifically designed to address it before the failure becomes irreversible. Speed in the build phase creates rebuild cycles in production that cost far more time than the audit would have.
Pitfall 2: Assuming source-system permissions transfer to retrieval
Permalink to “Pitfall 2: Assuming source-system permissions transfer to retrieval”Retrieval is a new query surface. It does not inherit ACLs from SharePoint or Confluence automatically. Explicitly map sensitivity tiers to vector store namespaces and apply metadata filters as retrieval defaults, before any user query reaches the knowledge base.
Pitfall 3: Chunking strategy chosen by default, not by content type
Permalink to “Pitfall 3: Chunking strategy chosen by default, not by content type”Default chunk sizes in LangChain and LlamaIndex work for demos. They do not account for the diversity of enterprise documents. Policies structured as numbered lists behave differently from narrative knowledge base articles or structured database exports. Test on a sample corpus first.
Pitfall 4: No evaluation set before launch
Permalink to “Pitfall 4: No evaluation set before launch”Teams build the pipeline and assume it works because the first few test queries returned plausible answers. Build 20–30 gold-standard Q&A pairs in Step 1 and test against them before any stakeholder demo. “It feels right” is not a quality signal.
Pitfall 5: No freshness monitoring post-launch
Permalink to “Pitfall 5: No freshness monitoring post-launch”Governance is treated as a launch task, not an operational one. Wire freshness checks and re-certification triggers in Step 7 before go-live. The knowledge base will not stay current on its own — source documents change, ownership shifts, policies are revised.
How Atlan accelerates LLM knowledge base governance
Permalink to “How Atlan accelerates LLM knowledge base governance”Without a governance substrate, Step 0 is entirely manual: spreadsheet inventories of document owners, hand-applied sensitivity tags, no automatic staleness detection. Most teams skip it because the cost is too high. Then they hit the 72% failure rate in production and spend significantly more time rebuilding than the audit would have cost.
Atlan’s context layer is, effectively, a pre-built Step 0. If your organization already uses Atlan as its data catalog for AI, the metadata required for a governed LLM knowledge base (ownership, lineage, sensitivity classification, certification status, freshness signals) already exists, attached to your data assets. You don’t build governance from scratch. You surface the governance you already have.
Atlan’s active metadata layer can trigger re-certification workflows automatically when source assets change, eliminating the manual freshness check job from Step 7. Because Atlan tracks lineage, you can trace any LLM answer back through its retrieved chunk to its source asset and verify that asset is still current, owned, and certified. Teams with an existing governed catalog compress Step 0 significantly, because document ownership, sensitivity classification, and certification status are already maintained in the catalog, not rebuilt from scratch for each AI project. The MCP server delivers governed context directly to AI models, so the metadata layer doesn’t sit adjacent to the knowledge base. It IS the knowledge base.
See Context Studio in action to understand how Atlan’s context layer surfaces certified, governed data for enterprise AI.
Real stories from real customers: how enterprises activate governed context for AI
Permalink to “Real stories from real customers: how enterprises activate governed context for AI”"We're excited to build the future of AI governance with Atlan. All of the work that we did to get to a shared language at Workday can be leveraged by AI via Atlan's MCP server…as part of Atlan's AI Labs, we're co-building the semantic layer that AI needs with new constructs, like context products."
Joe DosSantos, VP of Enterprise Data & Analytics, Workday
"Atlan is much more than a catalog of catalogs. It's more of a context operating system…Atlan enabled us to easily activate metadata for everything from discovery in the marketplace to AI governance to data quality to an MCP server delivering context to AI models."
Sridher Arumugham, Chief Data & Analytics Officer, DigiKey
The governance step you take before all others
Permalink to “The governance step you take before all others”Building an LLM knowledge base that works in production requires starting one step earlier than every existing guide suggests. Audit your sources before you touch an embedding model: certify documents for AI use, classify sensitivity, designate a single version of record, and set freshness thresholds. These decisions determine retrieval quality more than any downstream architectural choice.
The framework (audit, scope, connect, architect, embed, store, retrieve, monitor) is an operational discipline, not a project with a finish line. Wire freshness monitoring and re-certification triggers before go-live, or plan to rebuild when source documents inevitably change. Enterprise teams with an existing data catalog have a structural advantage: the governance layer is already built. The LLM knowledge base is not a new system. It is a new query surface over the governed metadata you already maintain.
FAQs about how to build an LLM knowledge base
Permalink to “FAQs about how to build an LLM knowledge base”1. How long does it take to build an LLM knowledge base?
Permalink to “1. How long does it take to build an LLM knowledge base?”For a scoped pilot (a single domain, governed source, 500 to 5,000 documents), expect 6–8 weeks from Step 0 through a working retrieval chain. Production-grade systems with freshness monitoring and access control typically reach 10–12 weeks. The largest time variable is Step 0 governance work: teams with an existing data catalog compress this to 1 week; teams without one may spend 3–4 weeks.
2. What causes LLM knowledge bases to give wrong answers?
Permalink to “2. What causes LLM knowledge bases to give wrong answers?”The primary cause is ungoverned source data: stale documents, duplicate versions, or content that was never certified as accurate. A 2025 study found hallucination rates drop to near zero when knowledge bases are restricted to curated, certified content, versus 52% hallucination on unvetted data. Retrieval architecture and prompt engineering are secondary levers. They cannot fix bad source data.
3. How do I keep my LLM knowledge base up to date?
Permalink to “3. How do I keep my LLM knowledge base up to date?”Wire a freshness check job (daily or weekly) that queries each source system for documents updated since last ingestion. Flag changed documents as needs_recertification and pause them from retrieval until re-reviewed by their owner. Active metadata platforms can automate this trigger-and-notify workflow so freshness monitoring runs without manual intervention.
4. How is an LLM knowledge base different from a vector database?
Permalink to “4. How is an LLM knowledge base different from a vector database?”A vector database is one component of a knowledge base: the storage and retrieval layer for embedding vectors. An LLM knowledge base is the full system: governed source data, ingestion pipeline, embeddings, vector store, retrieval chain, LLM response layer, and freshness monitoring. The vector database is the engine. The knowledge base is the vehicle.
5. Should I use RAG or fine-tuning for my enterprise knowledge base?
Permalink to “5. Should I use RAG or fine-tuning for my enterprise knowledge base?”For factual recall over a document corpus that changes over time, use RAG. Fine-tuning encodes knowledge into model weights at a point in time. It cannot be updated without retraining, and it is expensive to maintain. Fine-tuning is better suited to style adaptation and task specialization than to knowledge retrieval from a living document corpus.
6. How do I control access to sensitive data in an LLM knowledge base?
Permalink to “6. How do I control access to sensitive data in an LLM knowledge base?”Assign sensitivity tiers to every document at ingestion (Step 0), store documents in isolated vector store namespaces by tier, and apply metadata filters at query time keyed to the requesting user’s access level. Never mix sensitivity tiers in the same namespace. Rely on retrieval-time filtering, not post-retrieval redaction, which is both unreliable and untestable.
7. What is the difference between an LLM wiki and a RAG knowledge base?
Permalink to “7. What is the difference between an LLM wiki and a RAG knowledge base?”An LLM wiki structures knowledge as human-readable, LLM-accessible text rather than embedding vectors, bypassing the vector database entirely. RAG retrieves relevant chunks dynamically at query time. Both approaches require governed source content: a stale wiki is as unreliable as a stale vector store. The governance problem from Step 0 is architecture-agnostic.
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