audit/28_Groq_Advanced_Prompting.md

Topic 28: Groq Advanced Prompting Audit

Audit Date: 2026-02-01 Auditor: Agent Antigravity Scope: Advanced Prompt Patterns (CoVe, CoD, Prefilling)

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1. Prompt Pattern Architecture

The Sentinel System (via llm_client.py) implements sophisticated prompting patterns as First-Class Code Primitives, abstracting the complexity from the business logic.

1.1 Chain of Verification (CoVe)

Implementation: LLMClient.generate_verified Compliance: ✅ Pass. The system implements the 4-phase "Verification Loop" recommended in advanced literature:

1. Draft: Initial generation.
2. Plan: Model generates verification questions to challenge its own draft.
3. Execute: Model answers questions (reducing hallucination bias).
4. Verify: Final synthesis of Draft + Verification Answers.

# app/core/llm_client.py
async def generate_verified(self, ...):
    draft = await self.generate_structured(...)
    questions = await self.generate(..., role=ModelRole.SMART_REASONING)
    answers = await self.generate(..., role=ModelRole.SMART_REASONING)
    return await self.generate_structured(...)

1.2 Chain of Density (CoD)

Implementation: LLMClient.generate_dense Compliance: ✅ Pass. The system implements an iterative "Identify-and-Fuse" loop to create high-density summaries, essential for concise Telegram alerts.

# app/core/llm_client.py
async def generate_dense(self, ctx, rounds=3):
    for i in range(rounds):
        prompt = "... Identify missing entities ... Compress text ..."
        current_summary = await self.generate(prompt, ...)

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2. Structured Outputs vs. Prefilling

The user documentation highlights Assistant Prefilling (e.g., {role: "assistant", content: "```json"}) as a technique for enforcing formats.

Audit Finding: Sentinel preferentially uses Native JSON Schema (json_schema) or JSON Object Mode (json_object), which are generally superior and more robust than prefilling for data extraction tasks on Groq's modern models (Llama 3.3, Llama 3.1).

• Primary Strategy: response_format={"type": "json_schema", ...} (Hard enforcement).
• Secondary Strategy: response_format={"type": "json_object"}.
• Fallback Strategy: Prompt Injection ("Respond ONLY with valid JSON").

Recommendation: Keep the current json_schema approach as the primary method. Use "Assistant Prefilling" only if integrating legacy models that lack native JSON support, which is currently not the case.

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3. Threat Intelligence Patterns

The system uses Persona Priming via app/core/prompts.py (Topic 14), aligning with the "Role Channels" best practice.

• Role: Defined in ModelRegistry and prompts.py.
• Context: Injected via RAG (Topic 16) and Static Prefixes (Topic 25).
• Safety: Enforced via check_safeguard (Topic 06).

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4. Conclusion

The Sentinel System's prompt engineering is Enterprise-Grade. It doesn't just "ask" the model; it wraps requests in algorithmic verification loops (CoVe) and density optimization loops (CoD), maximizing the intelligence extracted from Groq's high-speed inference.

Status: PASSED.