AI Knowledge Base Platforms 2026: Glean vs. Notion AI vs. Custom RAG

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-tools-AI-knowledge-base-platform-2026-creators

1. A company has 30 engineers and uses standard SaaS tools like Slack, Drive, and Confluence. Their evaluation shows 'good enough' results on a 50-question test. What should they do?

   1. Hire 20 more engineers before deciding
   2. Build a hybrid approach with equal investment in both
   3. Build a custom RAG system to improve domain accuracy
   4. Buy an enterprise AI search product like Glean

2. What does 'answer attribution' refer to in AI knowledge base platforms?

   1. The system's confidence score for each response
   2. The ranking of search results by relevance
   3. The ability to trace answers back to specific source documents
   4. The process of training models on company data

3. A company needs their AI search to handle proprietary file formats from custom internal systems. What is required?

   1. Manual upload of files to the AI platform
   2. A higher subscription tier with the vendor
   3. A custom data connector built in-house
   4. Switching to a different vendor that supports all formats

4. What is the primary risk when AI search tools do not properly handle access control lists?

   1. Slower query response times
   2. Users being locked out of the search system
   3. Confidential documents leaking across teams that shouldn't see them
   4. The AI model becoming less accurate over time

5. A startup has 8 engineers and needs to connect to 15 different SaaS applications plus several legacy databases. They need highly accurate domain-specific responses. What approach makes sense?

   1. Buy the cheapest enterprise search tool available
   2. Use a general-purpose chatbot without any search
   3. Build a custom RAG system with domain tuning
   4. Outsource the entire knowledge management system

6. What limitation do purchased enterprise AI search products have compared to custom-built RAG systems?

   1. They limit how much you can tune retrieval for specific domains
   2. They require more engineering resources to maintain
   3. They cannot handle user authentication
   4. They cannot be integrated with mobile apps

7. Which capability is mentioned as something AI knowledge platforms do well out of the box?

   1. Training new AI models from scratch
   2. Indexing across multiple SaaS sources like Slack, Drive, and Confluence
   3. Replacing human customer support agents entirely
   4. Automatically writing code for new features

8. What does the 'wrong answer with high confidence' problem refer to?

   1. AI refusing to generate responses without citations
   2. AI models refusing to answer questions they don't know
   3. AI only answering questions with low-stakes topics
   4. AI giving incorrect answers while appearing very certain

9. Why should permission handling in AI search be validated on real data rather than demos?

   1. Demos may not accurately represent actual access control behavior and edge cases
   2. Demos are required by law to be different from production
   3. Demos use faster servers than production
   4. Real data is needed to train the AI model

10. What analytics capability do enterprise AI search tools typically provide?

    1. Code execution statistics
    2. Query analytics segmented by team
    3. Database storage usage metrics
    4. Network traffic monitoring

11. A company evaluates an AI search product using 50 questions about their industry. What is this evaluation method called in the lesson?

    1. A domain certification
    2. A 50-question eval
    3. A stress test
    4. A benchmark audit

12. Which statement best captures the lesson's premise about building versus buying RAG?

    1. RAG systems are obsolete and should not be used
    2. Building RAG is always better because it's free
    3. Building RAG is harder than vendors admit; buying limits tuning flexibility
    4. Buying is always safer for any company size

13. What permission-related capability do enterprise AI search platforms handle automatically?

    1. Handling permissions inheritance from source systems
    2. Generating new security certificates
    3. Enforcing data retention policies
    4. Creating user accounts

14. A pharmaceutical company needs AI search that achieves 95% accuracy on medical terminology — far higher than commercial products provide. What must they likely do?

    1. Use a general-purpose search engine instead
    2. Accept lower accuracy from commercial tools
    3. Wait for vendors to improve their products
    4. Build a custom RAG with domain-specific tuning

15. What is a key reason the lesson gives for why building RAG might be necessary even though it's difficult?

    1. It's cheaper than any commercial product
    2. To handle unusual data sources that vendors don't support out of the box
    3. Commercial products require no technical setup
    4. It provides complete immunity from errors