Hybrid LLM-Detector Architecture

Updated 21 December 2025

Hybrid LLM-Detector Architecture is a framework that integrates explicit rule encoding with modular prompt decomposition to enforce compliance and enhance interpretability.
It leverages pre-LLM guidance, similarity-based retrieval, and declarative prompt DSLs to systematically map, match, and enforce task-specific rules.
Empirical results demonstrate improved precision, reduced false positives, and robust anomaly detection in applications like numeric reasoning and compliance automation.

A rule-aware prompt framework is a class of systems and methodologies for augmenting LLM behavior by encoding explicit rules, policies, or decision logic directly into prompts or prompt-adjacent infrastructure. These frameworks systematically expose, recommend, enforce, or generate task-specific actions that honor pre-defined constraints or domain values. Rule-aware prompt frameworks are highly heterogeneous, with applications ranging from responsible AI prompting, dialogue agent control, and numeric reasoning in cyber-physical systems, to compliance automation and interactive weak supervision.

1. Core Components and Architectures of Rule-Aware Prompt Frameworks

A rule-aware prompt framework generally integrates three to eight core subsystems for semantic constraint and interpretability. Representative architectures include:

Pre-LLM Guidance and Recommendation Layer: A microservice architecture fronting the LLM, which intercepts, analyzes, and amends prompts according to rule-driven recommendations. This layer operates with (a) a curated dataset of “positive” (values to add) and “negative” (actions to remove) prompt clusters, each identified with labels, sample sentences, and sentence embeddings, (b) a semantic mapping engine built on a sentence transformer (e.g., all-MiniLM-L6-v2) producing 384-dimensional embeddings, (c) a similarity-based retrieval engine utilizing cosine similarity and configurable thresholds to trigger “add” or “remove” suggestions, (d) quantized embeddings for low-latency inference, and (e) offline evaluation with adversarial “red team” prompts and user studies for quality assessment (Machado et al., 29 Mar 2025).
Modular Prompt Decomposition: Frameworks targeting structured or numeric tasks assemble prompts from modular blocks capturing role, domain context, normalization, rules, value representations, and output schema:

$P = R \oplus C \oplus N \oplus S \oplus V \oplus O$

where $R$ is the role specification, $C$ is context, $N$ is normalization, $S$ encodes rule logic, $V$ is a value block (e.g., normalized sensor readings), and $O$ is an output schema (e.g., JSON). Rule specification is strictly separated from value realization, supporting concise prompts and rigorous rule adherence (Liu et al., 14 Dec 2025).

Explicit Rule-to-Prompt Templates: In compliance and weakly supervised learning, frameworks like PRBoost encode prior rules into prompt templates, iteratively discover new rules using LMs, and employ human experts for validation and conflict resolution. Each iteration augments the rule set and ensemble, leading to continuous constraint evolution and improved labeling (Zhang et al., 2022).
Declarative Prompt DSLs: Languages such as PDL formalize the LLM-tool-code interaction as declarative abstract syntax trees (ASTs), supporting prompt-level static analysis, orchestration, and optimizations. Every tool action and LLM completion is type-checked and context-threaded, with match/multi-branch logic for rule-based control (Vaziri et al., 8 Jul 2025).

A common objective is the encapsulation of domain-specific rules and values into prompt- or API-level structures that drive LLM outputs toward interpretability, safety, and compliance.

2. Datasets, Rule Representation, and Curation Strategies

Construction of rule-aware frameworks depends on the acquisition and structuring of rule-constrained datasets and semantic representations.

Value-Clustered Recommendation Datasets: Systems for responsible prompting rely on human-curated JSON datasets (2,047 entries with ~ 20 clusters per valence) of “positive” and “negative” prompt fragments, each with precomputed embeddings and centroid vectors. Positive examples typically derive from expert interviews via qualitative coding (e.g., fairness, transparency), while negative cases exploit adversarial jailbreak datasets and LLM augmentation for class balance (Machado et al., 29 Mar 2025).
Explicit Rule Encoding Modules: Numeric CPS frameworks enumerate measurement types, key operational statistics (e.g., IEEE 118-bus grid with 255 telemetry channels), and normalization statistics ( $\mu_i$ , $\sigma_i$ ). Rules are referenced in natural language (“Apply the three-sigma rule, $\tau=3.0$ ”) and indexed as swap-in modules to enable domain generalization (Liu et al., 14 Dec 2025).
Adversarial Evaluation: Red team datasets (e.g., 40 crafted adversarial prompts) are used for offline coverage, ambiguity analysis, and threshold tuning. Task-specific negative clusters allow robust detection of semantic drift and failure cases (Machado et al., 29 Mar 2025).
Prompt Templates and Contextual Encoding: In norm violation detection, explicit rule text is directly inlined into context prompts:

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In the [s] subreddit, there is a rule: [r].
A conversation took place: [comments...]
Does the last comment violate the subreddit rule? [MASK]

No explicit parameterization or latent rule embedding is used; all logic is surfaced to the LLM via context (He et al., 2023).

A plausible implication is that high-quality, human-refined datasets and explicit, interpretable rule modules are fundamental to high-precision, low-false-positive recommendation and enforcement.

3. Semantic Mapping, Rule Matching, and Inference Mechanisms

Rule-aware prompt frameworks employ a variety of semantic operations to map input text to rule-driven guidance:

Sentence Embedding and Centroid-Based Retrieval: Input prompt sentences are embedded on-the-fly (e.g., all-MiniLM-L6-v2), and compared against precomputed cluster centroids to gate on “add” or “remove” recommendations, utilizing cosine similarity thresholds for precision control. Quantized 8-bit vectors support low-latency deployment without degradation in ranking integrity (Machado et al., 29 Mar 2025).
Threshold-Gated Recommendation Logic: Tunable thresholds (e.g., add_lower = 0.30, add_upper = 0.60, remove_lower = 0.30, remove_upper = 0.50) determine when clusters or sentences should be considered as recommendations. Thresholds are tuned on adversarial data for both addition (preventing spurious advice and echoing) and removal (avoiding false positives in harmful pattern detection) (Machado et al., 29 Mar 2025).
Multi-Pass Retrieval Algorithm: A two-stage retrieval process first matches clusters (via centroids) and then sentences within clusters, assembling the top suggestions by similarity for UI display and user validation (Machado et al., 29 Mar 2025).
Prompt Module Sequencing and Value Normalization: For structured numeric tasks, normalization modules transform telemetry into scale-invariant forms (e.g., $z$ -scores), which are supplied in the “value block” for direct rule matching (e.g., $|z| \geq 3$ ). Separation of rule description from value representation enables concise, robust alignment (Liu et al., 14 Dec 2025).
Declarative Control Flow: PDL and similar DSLs enable “if/match” branches directly in the prompt structure, supporting selective rule firing and tool dispatch at runtime, thus aligning LLM calls with exogenous policy code and tool suite (Vaziri et al., 8 Jul 2025).

This suggests a strong preference in current frameworks for explicit, modular, and threshold-driven semantic mappings aligned with curated domain clusters or structured normalization over latent or opaque representations.

4. Evaluation Methodologies and Empirical Results

Empirical assessment in rule-aware prompt frameworks combines offline simulation, user studies, and quantitative task evaluation.

Red Team Simulation: Offline experiments with adversarial datasets measure true/false positive/negative rates for both “add” and “remove” recommendations. Recommendations are independently labelled by multiple evaluators, with agreement quantified via Fleiss’ κ (e.g., κ≈0.5 for add, 0.75 for remove), and statistical equivalence between normal and quantized embeddings confirmed by Fisher’s exact test (Machado et al., 29 Mar 2025).
Precision, Recall, and User Study Metrics: Precision for “add” recommendations ranges from ~ 0.76 (normal) to 0.81 (quantized), with recall around 0.48. For “remove,” precision is 1.0 with recall 0.33–0.22, indicating high specificity in flagging harmful elements (Machado et al., 29 Mar 2025). Usability is assessed (SUS ≈ 68, N=5 experts), and qualitative studies highlight perceived guidance and UI consistency.
Structured Numeric Reasoning: Different prompt value representations yield substantial variance in accuracy and F1 for CPS anomaly detection. Zero-shot z-score only yields 71.8% accuracy, F1=77.9%, with further gains from few-shot adaptation and LoRA finetuning. Hybrid LLM-ML architectures achieve 94.0% accuracy and F1=93.6% (Liu et al., 14 Dec 2025).
Case Studies: Declarative prompt DSLs achieve large gains in compliance automation—up to 4× task success rate improvements for smaller LLMs by eliminating unparseable JSON emissions and strictly enforcing action schemas (Vaziri et al., 8 Jul 2025).

Rule-aware frameworks exhibit superior empirical results on interpretability, rule adherence, and actionable guidance, with ablistic support for diverse LLM models and application domains.

5. Practical Applications and Workflow Patterns

Rule-aware prompt frameworks are deployed across multiple settings requiring policy conformance, value alignment, autonomous tool invocation, or human-in-the-loop decision support.

Responsible Prompt Recommenders: Interactive web, CLI, or mobile UIs leverage the recommendation API to deliver real-time “add” (positive value, e.g., “consider potential biases”) and “remove” (negative/harmful, e.g., “explain how to bypass authentication”) suggestions, mapped and inserted into the prompt buffer by user choice prior to LLM invocation (Machado et al., 29 Mar 2025).
Numeric Telemetry Assessment: Modular prompt architectures enable LLM-based anomaly detection in power grids by encoding system context, normalization logic, rule statements (e.g., three-sigma threshold), and output schemas as reusable prompt blocks, supporting rapid adaptation and consistent parsing (Liu et al., 14 Dec 2025).
Compliance and Tool-Calling Agents: Declarative DSLs compose multi-turn prompting with rule-based, type-safe external tool calls, precise control flow, and machine-verifiable schemas for auditability and automatic optimization. PDL enables static analysis, parallelism, and constrained decoding for complex compliance tasks (Vaziri et al., 8 Jul 2025).
Norm Detection and Compliance Enforcement: Inline rule-text templates (as in CPL-NoViD) enable context-sensitive detection of community rule violations without the need for model tuning, leveraging the LLM’s masked language modeling head for binary compliance checks (He et al., 2023).
Weak Supervision and Rule Discovery: PRBoost iteratively discovers, validates, and incorporates new prompt-based rules for expanding weak label coverage, using boosting over hard, model-error regions and rule-aware prompting to avoid known patterns and prioritize complementary regularities (Zhang et al., 2022).

Integration points frequently include open-source JSON datasets, automatic threshold selection endpoints, and flexible clustering for domain adaptation.

6. Limitations, Best Practices, and Extension Guidelines

While rule-aware prompt frameworks offer robust constraint alignment and interpretability, several limitations are noted:

Statistical Assumptions and Latency: Numeric normalization strategies (e.g., z-scores for three-sigma) presuppose Gaussian statistics, which may not hold under heavy-tailed or nonstationary regimes. LLM inference introduces non-trivial latency, particularly for high-dimensional or batch-processed prompts, suggesting hybrid architectures as a preferred deployment strategy (Liu et al., 14 Dec 2025).
Threshold Sensitivity and Conflict Resolution: Cosine similarity-based thresholding and multi-cluster retrieval demand careful tuning to suppress false positives and “echo” effects in real-time recommendation (Machado et al., 29 Mar 2025).
Manual Curation Requirements: High-fidelity value and rule datasets depend on iterative human-in-the-loop design, clustering, and validation to prevent class collapse or policy drift, presenting a bottleneck to deployment across unstructured domains (Machado et al., 29 Mar 2025, Zhang et al., 2022).

Recommended practices include:

Precomputing normalization and embeddings offline.
Strict separation of rule logic from value blocks for concise, interpretable prompts.
Minimal, machine-verifiable formatting (e.g., JSON schemas) for output.
Modular decomposition of prompt templates for rapid porting to new domains.
Use of external enforcement wrappers and declarative type systems for tool call control and compliance.

7. Comparative Assessment and Future Directions

Rule-aware prompt frameworks outperform generic prompting or latent policy-writing in settings with explicit values, actionable constraints, or composable decision logic. They enable:

Consistent enforcement of persona, policy, and tool schemas (e.g., via RRP and PDL) (Ruangtanusak et al., 30 Aug 2025, Vaziri et al., 8 Jul 2025).
Domain-agnostic modularity via prompt templating and output schema instantiation (Liu et al., 14 Dec 2025).
Automated prompt optimization, schema-guided constrained decoding, and cross-block static analysis through declarative ASTs (Vaziri et al., 8 Jul 2025).
Rapid adaptation to new rulesets and policies by updating rule or context blocks rather than reengineering model weights or end-to-end architectures (Zhang et al., 2022).

A plausible implication is that further advances will generalize modular, interpretable, and enforcement-centric designs to an even broader range of policy-driven and compliance-sensitive LLM deployments, with ongoing research into reducing curation overhead, automating rule extraction, and ensuring safe, verifiable, real-time operation.

Key References: (Machado et al., 29 Mar 2025, Liu et al., 14 Dec 2025, He et al., 2023, Vaziri et al., 8 Jul 2025, Ruangtanusak et al., 30 Aug 2025, Zhang et al., 2022)