DuckDuckGooze
December 9, 2025
IntraQAI Development Update: Speed vs. Accuracy Trade-offs
Current Development Status
I've been working on optimizing our search and Knowledge Assistant, specifically:
- Enhanced memory functionality – improved learning from past queries for better future responses
- Improved crawling capabilities – more thorough data collection and indexing
- Challenge: These improvements have significantly impacted response speed
The Core Question: Speed vs. Accuracy in AI
From a UI/UX perspective, we're facing a critical design decision. Users expect:
- Speed: Immediate responses (ideally ❤️ seconds)
- Accuracy: Correct, comprehensive answers
But these often conflict. More thorough analysis = slower responses.
IntraQAI's Evolution
Where We Started
- Core focus: Search and indexing
- Foundation was solid but not differentiated enough for market
Where We Are Now
- Primary focus: HR automation and policy creation
- Foundation: Search and indexing remain core infrastructure
- Value prop: Streamlined policy generation and HR workflows
Performance Issues We're Seeing
1. Search Performance Degradation
- Enhanced memory and crawling = slower responses
- Users notice the lag, especially for real-time queries
2. Document Generation Speed
- Similar slowdowns in policy and document creation
- Occurs when we hit a Knowledge Gap
3. Knowledge Gap Workflow
When the system lacks sufficient information:
- System detects the gap
- Generates a draft document
- Routes to human reviewer for approval
- Human fills in missing context
The problem: This workflow is slower than expected, creating friction in the user experience.
Questions for Discussion
-
Speed vs. Accuracy Balance
- HR policies require accuracy, but users also want quick turnaround
- Should we offer a "fast draft" vs. "thorough analysis" mode?
-
Knowledge Gap Handling
- Pre-cache common policy templates?
- Parallel processing for document generation?
- Better predictive modeling to reduce gaps?
-
UI/UX Solutions
- Progress indicators with time estimates?
- Async processing with notifications?
- Tiered service levels (instant, standard, deep)?
-
Market Positioning
- Compliance requires accuracy
- Adoption requires smooth UX
Next Steps: Liability Mitigation Focus
My immediate priority is liability mitigation for AI-generated policies.
The Critical Issue
Multiple advisors have flagged this as essential beyond standard Terms and Conditions:
- Risk: Employees acting on inaccurate AI-generated policies
- Liability: Company exposure if policies contain errors or compliance gaps
- Concern: T&Cs alone won't shield us from litigation
The Challenge
How do we ensure generated policies are legally sound while maintaining speed and usability?
Standard disclaimers (“AI-generated, review before use”) may not be sufficient for HR and compliance contexts.
Open Questions
- Verification Layer: What human-in-the-loop checkpoints are sufficient?
- Confidence Thresholds: Should we block publication below certain accuracy scores?
- Audit Trails: What documentation protects us if something goes wrong?
- Legal Language: What disclaimers or warnings need to be embedded in outputs?
How are you all handling liability for AI-generated compliance content? What safeguards have worked for you?
Additional Development Priorities
- Decide on speed and accuracy trade-offs for MVP
- Optimize the Knowledge Gap workflow
- Implement UX patterns that manage expectations
- Test with real HR scenarios to validate approach