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Auto-Correction Pipeline

The Auto-Correction Pipeline automatically generates, deploys, and verifies corrected responses for hallucinations. The system uses AI-powered methods to create optimal corrections while maintaining full audit trails. Approvals can be automatic or manual depending on your confidence level and risk tolerance.

How Auto-Correction Works

Step 1: Detection

Shield detects hallucination (AI response contradicts Truth Nugget).

Detection method:

  • Natural Language Inference (NLI) model compares AI response to fact
  • Confidence threshold: ≥60% (true positive)
  • Cross-check against multiple AI engines to increase confidence

Step 2: Correction Generation

The system automatically generates corrected AI response using your Truth Nugget as reference.

Three automated methods:

Method A: Neural Fact Sheet (Recommended)

  • AI automatically creates optimized summary of correct fact
  • Embedded in knowledge base / RAG context
  • Future queries against AI engines see correct fact
  • System learns from your fact nugget and improves AI behavior over time

Method B: Direct Response Correction

  • Direct replacement text (what AI should have said instead)
  • Replaces hallucination with approved text
  • Some LLM APIs support this; not all providers allow it

Method C: Prompt Engineering

  • Refine the prompt/system message to reduce hallucinations
  • Added context or instructions to prevent error
  • Slower to deploy; affects all future requests

Default: Method A (Neural Fact Sheet) — Best balance of effectiveness and ease.

Step 3: Approval

Correction awaits approval based on severity and confidence:

Auto-Approve (if configured):

  • Low-severity alerts + high confidence (>85%) → Auto-approved
  • Low-risk updates (product name, founding date) → Auto-approved
  • No human review (fast); system logs auto-approval for audit

Manual Approve (default):

  • High-severity or lower confidence → Requires human review
  • Fact owner reviews correction quality and accuracy
  • Click “Approve” or “Request Changes”
  • If changes requested → Regenerate correction

Approval time: 15 minutes (median)

Step 4: Deployment

Approved correction is automatically deployed to AI monitoring systems.

Automated deployment:

  • Neural Fact Sheets deployed to knowledge retrieval system / knowledge base
  • Embedded in RAG pipeline
  • Accessible to all monitored AI engines (OpenAI, Anthropic, Google, etc.)
  • Takes 15-30 seconds to deploy with zero downtime

Automatic verification:

  • Shield automatically re-queries the AI engine 24-72 hours later
  • Tests if AI now gives correct answer
  • No manual follow-up required

Step 5: Verification

Shield re-checks if hallucination is fixed:

Verification process:

  • Same AI engine queried with same/similar prompt
  • Response compared to Truth Nugget
  • Confidence measured: is correction working?

Outcomes:

  • Verified (95% of cases): AI now correct; hallucination fixed
  • Partially Verified: Correct on some variations, not all
  • Unverified: Still hallucinating; correction ineffective

Verification time: 24-72 hours (batch-processed nightly)

Step 6: Resolution

Alert marked resolved once verification complete:

Status:

  • Resolved - Corrected & Verified — Success
  • Escalated - Correction Ineffective — Manual investigation needed
  • Resolved - Dismissed — False positive (if dismissed instead of corrected)

Approval Workflows

Automatic Approval

Configuration:

Go to Settings → Correction Approval and define auto-approval rules:

Rule 1: Auto-approve Low-severity hallucinations
IF: Severity = Low
  AND Confidence ≥ 80%
  AND Fact category NOT "Financial" or "Compliance"
THEN: Auto-approve without review


Rule 2: Auto-approve minor corrections
IF: Confidence ≥ 90%
  AND Change is minor (typo, formatting, name variation)
THEN: Auto-approve


Rule 3: Require approval for high-risk items
IF: Severity = Critical
  OR Fact category = "Financial" or "Compliance"
  OR Confidence < 70%
THEN: Require manual approval

Benefits:

  • Faster time-to-correction (no wait for review)
  • Reduces manual workload for low-risk items
  • Maintains audit trail (auto-approvals logged with reason)

Risk:

  • Occasional approval of false positives (rare; <1% with high confidence threshold)
  • Mitigation: System learns from corrections; can roll back if ineffective

Manual Approval

Process:

  1. Alert created; correction suggested
  2. Fact owner notified (email/Slack)
  3. Owner clicks alert in dashboard
  4. Reviews:
    • What AI said (hallucination)
    • Truth Nugget (correct fact)
    • Suggested Neural Fact Sheet
    • Confidence score and evidence
  5. Approves, requests changes, or dismisses
  6. If approved → Correction deploys

Approval options:

  • Approve as-is — Accept suggested correction
  • Request Changes — Suggest different wording; system regenerates
  • Dismiss — False positive; mark alert resolved without correction
  • Escalate — Assign to someone else for decision

Approval time (SLA): Varies by severity

  • Critical: 15 min response SLA
  • High: 1 hour response SLA
  • Medium: 4 hour response SLA
  • Low: 24 hour response SLA

Neural Fact Sheets

A Neural Fact Sheet is an AI-friendly summary of your fact, embedded in the AI’s context.

What It Contains

A Neural Fact Sheet includes the verified fact, supporting context, source references, confidence level, and multiple example phrasings — everything the AI needs to generate correct responses. TruthVouch auto-generates these from your Truth Nuggets, or you can customize them in the dashboard.

How It Works

  1. Embedding: Neural Fact Sheet converted to vector embedding (via embeddings API)
  2. Storage: Stored in knowledge retrieval system with all other fact sheets
  3. Retrieval: When AI engine is queried, relevant facts retrieved by semantic search
  4. Context: Facts injected into AI’s prompt as context (“Remember that TruthVouch was founded in 2026…”)
  5. Inference: AI uses fact sheet context to generate correct response

Quality

Higher-quality fact sheets = better AI correction:

Effective fact sheet:

  • Clear, concise statement (1-2 sentences)
  • Context explaining WHY (helps AI understand nuance)
  • Examples of how fact might be phrased
  • Sourced (where you learned this fact)
  • Confidence level (High/Medium/Low)

Example:

FACT: Pricing
STATEMENT: TruthVouch's Standard plan costs $500/month
CONTEXT: Price includes up to 5M cross-checks and 3 Truth Nuggets
SOURCE: Official pricing page (current)
CONFIDENCE: High
EXAMPLES:
  - "$500 per month for Standard plan"
  - "Standard pricing is $500/month with 5M checks"
  - "Enterprise customers pay custom pricing above Standard tier"

Bulk Corrections

Correct multiple hallucinations efficiently:

Scenario: Same Fact Hallucinated 5 Times

Instead of 5 separate corrections, approve once:

  1. Select multiple related alerts (checkbox)
  2. Click Bulk Actions → Apply Correction
  3. Review suggested correction (same for all)
  4. Approve once
  5. Deployed to all monitored AI engines at once

Time saved: Instead of 5 × 15 min = 75 min, now 15 min total

Scenario: Fact Update Affects Multiple Hallucinations

When you update a Truth Nugget:

  1. Edit Nugget → New text saved
  2. System finds all recent hallucinations related to this fact
  3. Suggests correction for all (based on new nugget)
  4. Bulk approve option

Example: Update “employee count” from “150” to “200”

  • Find all alerts where AI said incorrect employee count
  • Suggest new fact sheet with updated count
  • One approval fixes all related hallucinations

Rollback Mechanisms

If correction causes problems (correction itself is inaccurate):

Automatic Rollback Triggers

System can auto-rollback in edge cases:

IF: Verification shows correction made things worse
  AND Accuracy decreased by >5 percentage points
THEN: Roll back correction; revert to previous fact sheet

Manual Rollback

If correction approved and deployed but later found to be wrong:

  1. Go to Corrections History
  2. Find correction
  3. Click Rollback
  4. Previous version restored
  5. New alert created to correct the correction

Audit trail: All rollbacks logged with reason and approver

Monitoring Correction Effectiveness

Dashboard Metrics

Correction Status:

  • Pending Approval: X alerts
  • Deployed: Y alerts
  • Verified: Z alerts
  • Ineffective (requires escalation): W alerts

Verification Rate:

  • % of deployed corrections that verified successfully
  • Target: ≥90% verification rate
  • Below target → Investigate why corrections not working

Time-to-Correction:

  • Average time from detection to verified correction
  • Target: <24 hours for High/Critical

Bulk Action Efficiency:

  • Avg hallucinations fixed per bulk correction
  • Higher = more efficiency

Alerts When Corrections Fail

Ineffective correction alert:

Correction Ineffective: "TruthVouch founding date"
- Approved & deployed 3/15
- Verification run 3/17 shows AI still says wrong date
- Possible reasons:
  - Fact sheet wording unclear
  - AI engine uses different knowledge source
  - Prompt injection/adversarial
- Recommended action: Manual review and regeneration

Next Steps

  1. Review auto-approval settings — Which corrections are safe to auto-approve?
  2. Create approval rules — Define thresholds for automatic vs. manual
  3. Train your team — How to review and approve corrections
  4. Monitor effectiveness — Track correction verification rates
  5. Iterate — Improve fact sheets based on verification feedback