Stale Data is Wrong Data

Imagine a customer service bot that answers questions about "Current Product Prices." If a manager updates a price in a document at 10:00 AM, but the AI doesn't "Sync" until 10:00 PM, the AI will give thousands of incorrect answers. For the AWS Certified Generative AI Developer – Professional, you must know how to build a "Living" Knowledge Base.

In this lesson, we master Real-time Synchronization using event-driven architectures.

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1. Trigger-Based Synchronization

Instead of "Polling" (checking every hour), we use Push notifications.

• Storage Layer: A user uploads a file to Amazon S3.
• The Event: S3 triggers an S3 Event Notification.
• The Executor: A AWS Lambda function receives the event and calls the Bedrock StartIngestionJob API.
• Result: The new data is available for retrieval in seconds, not hours.

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2. Incremental vs. Full Sync

As a Professional Developer, you must be efficient.

• Full Crawl: Re-processes every document in the bucket. (Expensive, slow, but ensures 100% consistency).
• Incremental Sync: Only processes the files that have changed since the last sync. (Cheap, fast, but requires careful tracking).

The Pro Path: Bedrock Knowledge Bases handle incremental sync automatically. You just need to provide the S3 prefix, and the service determines which files are new.

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3. Handling "Deletes" (The Hard Part)

If you delete a PDF from S3, does it disappear from the AI's "Brain"?

• On-Demand: If you delete the file and run a sync, Bedrock recognizes the file is gone and deletes the corresponding vectors from OpenSearch.
• The Catch: This only works if you are using the Bedrock-managed Ingestion pipeline. If you built a custom pipeline, you must manually run an OpenSearch: Delete command to remove the stale embeddings.

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4. Event-Driven AI Architecture

graph LR
    U[User/App] -->|Upload| S3[S3 Bucket]
    S3 -->|PutObject Event| EB[Amazon EventBridge]
    EB -->|Rule| L[Lambda: Sync Trigger]
    L -->|StartIngestionJob| B[Bedrock KB]
    B -->|Update| OS[(OpenSearch)]
    
    style EB fill:#ff9900,color:#fff

Using Amazon EventBridge instead of direct S3-to-Lambda allows for more complex logic (e.g., "Only sync if the file size is < 100MB" or "Send an email alert if the sync fails").

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5. Dealing with "Burst" Updates

What if a user uploads 5,000 files at once (a folder drag-and-drop)?

• The Risk: 5,000 individual sync jobs starting simultaneously, causing an API bottleneck.
• The Solution: Debouncing. Your Lambda function should wait 30 seconds to see if more files are coming, then trigger a single sync job for the whole batch.

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6. Pro-Tip: Version Control for Knowledge

For highly regulated industries, you need to know why the AI gave an answer at a specific time.

• Use S3 Versioning.
• When the AI retrieves a chunk, store the S3 Version ID in your audit logs.
• If a client complains about a response from last Tuesday, you can "travel back in time" to see exactly what the document looked like at that moment.

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Knowledge Check: Test Your Sync Knowledge

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Summary

A Knowledge Base that is out of sync is a liability. By using EventBridge, Lambda, and Incremental Sync, you ensure your AI is always up-to-date. In the final lesson of Module 19, we move to Data Quality and Evaluation at Scale.

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Next Lesson: Garbage In, Garbage Out: Managing Data Quality at Scale