Feat/haloguard pro scaffolding 1

[Kumarvels]

🧩 How HaloGuard Pro Controls Hallucinations

1. Knowledge Bases as Source of Truth

• All truths are stored in structured JSON (facts/*.json).

• Each entry contains:

  • correct: the verified fact

  • incorrect_patterns: regex/string matches for common hallucinations

  • auto_correct: whether to automatically replace

  • alert_to_admin: whether to escalate

👉 Instead of semantic understanding, the framework relies on string and regex pattern detection. This is deterministic and avoids model drift.

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2. Verification Flow

1. LLM generates output
   Example: "Einstein died in 1950"

2. HaloGuard checks output against KB

   • Regex finds "Einstein died in 1950"

   • Mapped to "einstein" entry in general.json

3. System auto-corrects (if enabled)
   → "Albert Einstein died on April 18, 1955, in Princeton, New Jersey"

4. Audit logs record both original and corrected versions

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3. Framework Design Choices

• FastAPI + Pydantic → lightweight API wrapper.

• Regex entity extraction → for detecting names, years, currencies, percentages.

• No embeddings/vector DB → avoids high compute and fuzzy matches.

• Audit log pipeline → logs/haloguard.log stores every correction (compliance-ready).

• Configurable via .env → admin can turn auto-correct on/off by domain.

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🔄 Comparison to Other Hallucination-Reduction Frameworks

Framework | How it Works | Pros | Cons -- | -- | -- | -- HaloGuard Pro | Exact string/regex match against curated KB | ✅ Ultra-fast, ✅ CPU-only, ✅ Transparent | ❌ Limited coverage, ❌ Needs manual KB updates RAG (Retrieval-Augmented Generation) | Embed query, search vector DB, re-inject | ✅ Broad coverage, ✅ Flexible | ❌ Expensive infra, ❌ Still possible hallucinations LLM-as-a-Judge | Another LLM verifies output | ✅ Contextual, ✅ Handles nuanced cases | ❌ Doubles cost, ❌ Secondary LLM may hallucinate Constraint Decoding | Guide LLM generation via rules/templates | ✅ Stops some hallucinations at generation-time | ❌ Requires fine-tuning or model access Human-in-the-Loop Review | Domain experts check outputs | ✅ Highest accuracy | ❌ Slow, ❌ Not scalable

HaloGuard’s philosophy = “less is more”: instead of trying to eliminate all hallucinations, it catches only the high-risk, known ones.

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⚙️ Production Integrations

• Middleware: Wrap HaloGuard between user and LLM API.

• Multi-turn support: Feed prev_messages if you want conversational context.

• Domain toggle: Use different KBs (medical.json, finance.json, etc.) depending on chatbot type.

• Monitoring: Health checks via scripts/health_check.sh, fact updates via scripts/update_facts.sh.

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🚀 Extending the Framework

1. Add new facts/*.json entries per domain.
   Example: climate.json, history.json.

2. Improve regex/entity extraction (names, dates, numbers).

3. Build a UI dashboard for:

   • Viewing flagged hallucinations

   • Adding new truth entries

   • Monitoring logs in real time

4. (Optional) Hybrid Mode: Use HaloGuard for hard rules + fallback to RAG/LLM-as-Judge for gray areas.

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✅ Bottom line:
HaloGuard Pro isn’t aiming to “understand” like RAG/LLM evaluators — it’s a firewall against known dangerous hallucinations (medical, legal, financial, compliance-critical). Its strength lies in being predictable, cheap, and production-hardened.