# Outdated Knowledge Base

## The Problem

Knowledge base contains stale information that's been updated in source systems, causing AI to provide outdated answers.

### Symptoms

* ❌ AI cites old pricing/features
* ❌ Refers to deprecated APIs
* ❌ Misses recent updates
* ❌ Inconsistent with current docs
* ❌ Users correct AI with newer info

### Real-World Example

```
January: Document states "API v1 is current"
→ Ingested into knowledge base

March: API v2 released, docs updated
→ Knowledge base NOT updated

User query (April): "What's the current API version?"
AI: "The current API version is v1"

Wrong - knowledge base 3 months stale
```

***

## Deep Technical Analysis

### Sync Frequency Issues

**One-Time Ingestion:**

```
Common mistake:
→ Ingest docs once at setup
→ Never refresh
→ Knowledge base frozen in time

Source docs evolve:
→ New features added
→ Bugs fixed
→ Policies updated
→ RAG oblivious to changes
```

**Manual Re-Ingestion:**

```
Admin must remember to:
→ Re-sync periodically
→ Error-prone (forgotten)
→ Unpredictable staleness
```

### Incremental Sync Challenges

**Delta Detection:**

```
Which documents changed?
→ Check last_modified timestamp
→ Only re-ingest modified docs

But:
→ Some sources don't expose last_modified
→ Metadata unreliable
→ Must re-ingest everything (slow)
```

**Version Conflicts:**

```
Doc version 1: Embedded as chunks A, B, C
Doc version 2: Updated

Options:
A) Delete old chunks, add new → Clean but complex
B) Add new chunks, keep old → Duplicate/conflicting info

Need versioning strategy
```

### Real-Time vs Batch

**Batch Sync (Daily):**

```
Pros:
+ Simple
+ Predictable load

Cons:
- Up to 24h staleness
- Critical updates delayed
```

**Real-Time Sync (Webhooks):**

```
Source system sends webhook: "Doc X updated"
→ Immediately re-embed and update

Pros:
+ Always current
+ No staleness

Cons:
- Complex infrastructure
- Webhook reliability
- Burst load handling
```

### Document Deletion Handling

**Deleted Docs:**

```
Source: Document removed
Knowledge base: Still has old chunks

AI cites deleted/non-existent document:
→ User: "That link is dead"
→ Trust eroded

Must detect deletions and remove chunks
```

**Soft Deletes:**

```
Some systems soft-delete (mark as deleted):
→ Doc still exists but hidden
→ API may still return it
→ RAG may ingest deleted content

Filter at ingestion: WHERE deleted_at IS NULL
```

### Timestamp Metadata

**Last Updated Tracking:**

```
Store with each chunk:
{
  vector: [...],
  metadata: {
    document_id: "doc_123",
    last_updated: "2024-01-15T10:00:00Z",
    source_url: "https://..."
  }
}

Benefits:
→ Know freshness of each chunk
→ Surface last_updated in AI response
→ User can assess relevance
```

***

## How to Solve

**Implement automated incremental sync (daily or real-time webhooks) + track document last\_modified timestamps + delete outdated chunks when source updated + use webhook-triggered re-ingestion for critical docs + display "last updated" timestamp in AI responses + monitor sync lag metrics.** See [Data Freshness](/rag-scenarios-and-solutions/accuracy/stale-data.md).


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