CLAIRIFY: Verifier-Assisted Iterative Prompting

• CLAIRIFY is a paradigm that employs iterative cycles of generation and verification, using human or automated verifiers to refine LLM outputs.
• It systematically addresses errors and ambiguities through multi-agent feedback loops, enhancing performance in dialogue, coding, and data extraction tasks.
• The methodology relies on recursive feedback and formal verification to achieve robust intent disambiguation, semantic fidelity, and reliable task execution.

Verifier-Assisted Iterative Prompting (CLAIRIFY) denotes a formalized paradigm for incrementally refining the outputs of LLMs or dialogue agents by employing explicit cycles of generation and verification. In CLAIRIFY, a verifier—either human or automated—critically examines candidate outputs, identifies errors or ambiguities, and supplies targeted feedback that guides subsequent iterations. This architecture elevates prompt-based interaction from static input–output mapping to an adaptive loop, enabling robust intent disambiguation, semantic fidelity, and reliable execution in diverse domains such as conversational AI, program synthesis, code generation, information retrieval, and structured data creation.

1. Conceptual Foundations of Verifier-Assisted Iterative Prompting

Verifier-assisted iterative prompting formalizes the separation between the generation phase (LLM, dialogue agent, or structured task planner) and the verification phase (rule-based system, domain expert, or learned verifier). The key process is recursive: after each generation, the verifier detects errors or unresolved ambiguities, formulates constructive feedback, and the system is re-prompted—either to repair the exact deficiencies or to clarify intent through dialogue. The cycle repeats until the verifier confirms correctness, all ambiguities are resolved, or a stopping criterion is reached.

A generic mathematical formalization present in the literature (Skreta et al., 2023, Han et al., 2023) covers this loop as: $$\begin{aligned} \text{Candidate}_{n+1} = \text{Generator}(\mathcal{L}, x, \text{Candidate}_n, \text{Feedback}_n) \ \text{Feedback}_{n+1} = \text{Verifier}(\text{Candidate}_{n+1}) \ \text{Repeat until}~\text{Feedback}_{n+1} = \emptyset \end{aligned}$$ where $\mathcal{L}$ denotes task-specific language constraints, $x$ is the raw input, and $\text{Feedback}_n$ covers detected errors, missing actions, constraint violations, or ambiguity signals.

This process generalizes across modalities: natural language (clarification questions in QA/chat, (Lautraite et al., 2021, Murzaku et al., 19 Mar 2025)), structured programming (syntactic/semantic verification in robotics and ABMs, (Skreta et al., 2023, Niu et al., 2024)), code intent resolution (Darji et al., 28 Jul 2025), and even creative or analytical domains with ambiguity (Marozzo, 5 May 2025).

2. Iterative Dialogue and Clarification Modules

A prototypical application appears in open-domain conversational systems or enterprise QA assistants (Lautraite et al., 2021, Murzaku et al., 19 Mar 2025). Here, verifier-assisted iterative prompting unfolds as multi-stage clarification:

1. Direct answer: If confidence in intent classification exceeds threshold $T$, answer immediately.
2. Confirmation: If confidence ${C<T}$ but above lower bound, paraphrase top intent and seek user affirmation ("Did you mean...?")
3. Suggestion: Upon rejection, offer alternative intents by decomposing the query using TFIDF keywords or domain-specific signals.
4. FAQ fallback: If resolution fails, provide generic, high-coverage fallback topics.

Each step acts as a verifier-guided gating function—successively resolving user intent while minimizing erroneous replies and fallback frequency. Such systems empirically achieve superior F1, satisfaction, and intent coverage compared to vanilla fallback or single-shot schemes (micro-F1 near 0.92 vs. 0.80–0.86, (Lautraite et al., 2021)).

In modular agent frameworks like ECLAIR (Murzaku et al., 19 Mar 2025), each agent (e.g., sentence-level, product, entity, concept detectors) serves as a specialized verifier, whose outputs seed interactive clarification cycles with tailored questions.

3. Error-Guided Generation and Structured Verification in Data-Scarce Domains

CLAIRIFY’s strength is highlighted in structured language domains where LLMs are initially unfamiliar—e.g., chemistry DSLs, agent-based modeling (Skreta et al., 2023, Niu et al., 2024). Here, the verifier scrutinizes both syntactic validity (XML compliance, tag usage) and constraint adherence (resource availability, hardware limitations, simulation results):

• Errors (e.g., "missing property", "unknown tag") are returned as explicit feedback, reformulated in the next prompt.
• Each round incrementally repairs the output, often yielding state-of-the-art results even without extensive task-specific training (see Figures in (Skreta et al., 2023): 105/108 success on Chem-RnD; preference rates >70% by experts; full coverage on Chem-EDU).

In agent-based modeling, verifier-level1 (program correctness) and verifier-level2 (solution effectiveness, chain-of-thought prompts) are employed. Simulation results, objective criteria, and extracted relations work in concert to drive iterative optimizations of model code and behavior (Niu et al., 2024).

4. Ambiguity Resolution and Reasoning in Information Retrieval

Targeted ambiguity modeling further increases clarification quality in IR and dialog systems. Taxonomies such as those in (Tang et al., 16 Apr 2025) (Semantic, Generalize, Specify) guide a chain-of-thought prompt—first reasoning about ambiguity type, then generating an action-guided clarifying question (Ambiguity Type-CoT, AT-CoT). This approach ensures not only higher clarifying question quality (e.g., BERTScore from ~80 to ~82) but also improved retrieval effectiveness in simulated multi-turn sessions.

Verifier integration can be operationalized either by post-hoc checking whether clarifications address ambiguity types or by deploying a feedback loop where unresolved ambiguities trigger successive refinement (Tang et al., 16 Apr 2025, Murzaku et al., 19 Mar 2025).

5. Progressive Cutting-Search and Dialogue Architectures in Coding and Data Analysis

Recent work formalizes ambiguity resolution in coding assistants as an iterative cutting-search (Marozzo, 5 May 2025, Darji et al., 28 Jul 2025). After ambiguity detection (e.g., via classifiers such as DistilBERT), targeted clarification queries are posed ("Is 'last month' previous calendar month or last 30 days?"), and user feedback narrows interpretations one by one.

The procedure is recursively defined (see algorithm in (Marozzo, 5 May 2025)):

• Detect ambiguities $A = \{A_1, ..., A_n\}$
• For each $A_i$, loop clarification until resolved via options $R_i$
• Final solution $S$ is generated plus representative edge-case examples

This interface, mimicking expert code review, achieves higher rated precision, focus, and faithfulness (likert scales and effect sizes in (Darji et al., 28 Jul 2025): $d>1.2$, $p<0.001$ on quality metrics; 82% favorable ratings for clarification-enabled answers).

6. Empirical Results, Performance, and Trade-offs

Verifier-assisted iterative prompting has consistently yielded notable gains:

• Micro-F1 and coverage: 0.92 vs. 0.80–0.86 for dialogue QA (Lautraite et al., 2021).
• CodeBLEU and solution optimality: +18.7% modeling quality, +38.1% improvement in ABM problem-solving (Niu et al., 2024).
• Graph-based tasks: +26% improvement over naive generation (Han et al., 2023), cost-efficiency through offline correction.
• User experience: Dialogue rounds reduced, higher CLIP scores for image–prompt alignment (He et al., 25 Jan 2025), and significantly elevated user ratings for correctness and focus in coding (Darji et al., 28 Jul 2025, Marozzo, 5 May 2025).

Performance is contingent on verifier quality: external, sound verifiers yield distinct improvements in reasoning (e.g., 16% → 40% correct graph colorings in (Stechly et al., 2023)), whereas self-critiquing by LLMs alone remains unreliable or may worsen hallucinations and convergence.

A trade-off emerges between iterative overhead (latency) and final output fidelity. Systems with multi-agent verifiers or extended clarification cycles may incur longer interaction time (average 4.3 rounds for image synthesis, 133s per coding clarification) but yield substantially more user-aligned, reliable results.

7. Limitations, Extensions, and Research Directions

Limitations center on:

• Verifier accuracy: Classifier misjudgments can lead to unnecessary clarification cycles or missed errors (Darji et al., 28 Jul 2025).
• Synthetic dataset bias: Training on artificial examples may limit generalizability to organic input (Darji et al., 28 Jul 2025).
• User patience and real-time latency: Iteration costs may impact user acceptance in production.
• Automation of feedback loops: Human-in-the-loop feedback remains costly compared to autonomous rule-verification or multi-agent verification (Murzaku et al., 19 Mar 2025).

Emerging directions involve:

• Structured ambiguity modeling (AT-CoT), reasoning-taxonomy-guided clarification (Tang et al., 16 Apr 2025),
• Task-adaptive, joint training of generator–verifier pairs for multi-answer QA, code generation, and structured graph tasks (Sun et al., 2023, Han et al., 2023),
• Generalization to data-scarce domains using in-context learning with formal representations (Niu et al., 2024),
• Leveraging mutual information proxies (e.g., CLIP Score) for semantic alignment in creative generation (He et al., 25 Jan 2025),
• Hybrid architectures blending rule-based and neural verification to reconcile fast reasoning with deep semantic inspection (Stechly et al., 2023).

Summary Table: Key CLAIRIFY Use Cases and Performance

Application Domain	Verifier Mechanism	Notable Outcomes
Conversational QA (Lautraite et al., 2021)	Human confirmation, TFIDF keyword suggestions	Micro-F1 0.92, 92% correct answers
Structured program synthesis (Skreta et al., 2023)	Rule-based static analyzer	105/108 experiment coverage, expert preference
IR clarification (Tang et al., 16 Apr 2025)	Ambiguity type-based reasoning, AT-CoT	+2–3% BERTScore, improved nDCG@10, MRR@10
Coding assistants (Darji et al., 28 Jul 2025)	Classifier + query clarifier	82% favorable precision, $p < 0.001$
Agent-based modeling (Niu et al., 2024)	Multi-level verification, CoT prompting	+18.7% modeling, +38.1% solution scores

Verifier-Assisted Iterative Prompting (CLAIRIFY) formalizes the generation–verification loop into a modular architecture that incrementally drives LLM outputs toward precision, transparency, and domain-targeted alignment. Across domains, its recursive structure—anchored in explicit error feedback and multi-agent ambiguity detection—substantiates high empirical performance gains and lays groundwork for scalable, reliable, and interpretable AI systems.