Advanced Answer Generation Techniques for Airline Chatbots

Abstract

AI-powered chatbots are transforming customer service in aviation, where accuracy isn't just important, it's critical. This document explores techniques and evaluation methods for building reliable answer generation systems within airline chatbots.

The challenge?

Large language models love to hallucinate. They misrepresent context, invent facts, and ignore retrieved information when generating responses. In aviation, where incorrect baggage policies or flight change rules can cost airlines millions, this isn't acceptable.

We examine practical, low-cost strategies using prompt engineering and design evaluation frameworks tailored to retrieval-augmented generation (RAG) pipelines. Our analysis draws on recent research in hallucination detection, answer faithfulness, and LLM evaluation. We propose methodologies for domain-specific dataset creation and benchmarking, essential tools missing from current airline-specific resources.

The goal is simple: guide implementation of robust, scalable chatbots capable of supporting real-world airline operations without the hallucination headaches.

1. Problem Statement

Designing a reliable QA chatbot for the airline industry introduces several domain-specific and technical challenges that must be addressed from the outset:

• High requirement for accuracy: Airline customer queries often involve time-sensitive or policy-critical information (e.g., flight changes, baggage rules, refunds). Incorrect answers can lead to user frustration, compliance risks, or financial penalties.
• LLM limitations: While large language models are strong at generating fluent responses, they frequently:
  • Hallucinate facts or make up information.
  • Ignore important context retrieved by the RAG system.
  • Rely on pretraining knowledge instead of grounded content.
• Lack of domain-specific benchmarks: Most available QA benchmarks (e.g., Natural Questions, MedHallu, FinanceBench) are either open-domain or targeted at medical, legal, or financial domains. No established datasets or benchmarks exist for airline-specific use cases.
• Need for scalable evaluation: A practical evaluation strategy must:
  • Detect hallucinations.
  • Measure faithfulness and correctness of answers.
  • Support automated scoring for rapid iteration.
  • Integrate with CI/CD pipelines for ongoing QA during development.
• Feasibility over complexity: Many high-performance QA pipelines rely on complex models, fine-tuning, or multi-stage reranking. These are expensive to build and maintain. Early stage airline chatbots need cost-effective techniques that can be deployed using general-purpose models and cleanly structured prompts.
• Custom dataset requirement: To measure quality accurately in this domain, a tailored dataset must be built using airline documents, user-relevant questions, and annotated answers, enabling internal benchmarking that reflects real world usage.

2. Research Questions

A. Answer Generation Techniques

1. What are the main existing approaches for generating accurate and reliable answers in a retrieval-augmented chatbot?
2. Which attributes (e.g., hallucination reduction, cost-efficiency, implementation complexity, compatibility with RAG) are used to compare answer generation techniques?
3. How do these techniques perform across those attributes in terms of quality, scalability, and robustness in an airline domain?
4. Which techniques should be selected for the first version of the airline chatbot and why are they most appropriate for early deployment?

B. Evaluation Methods

1. What are the primary evaluation methods and benchmarks used to assess the performance of QA chatbots, particularly in detecting hallucinations and measuring answer quality?
2. Which criteria (e.g., faithfulness, correctness, hallucination rate, robustness, LLM-human agreement) are used to compare evaluation methods and datasets?
3. How do selected benchmarks and evaluation strategies perform across those criteria, especially for domain-specific or RAG-based systems?
4. Which evaluation flow and tools should be implemented for the airline QA chatbot, and why do they best support accuracy, maintainability, and CI/CD integration?

Techniques

In the architecture of a QA chatbot, the answer generation step plays a critical role in shaping the user experience and ensuring factual correctness. Once a user's intent is identified and relevant documents are retrieved, the system must generate a coherent, accurate, and contextually grounded response. This is particularly important in domains like aviation, where misinformation about policies, legal terms, or procedures can have significant operational and reputational consequences. While large language models (LLMs) have shown strong capabilities in generating fluent text, they are also prone to hallucinations, bias toward pre-trained knowledge, and misinterpretation of retrieved context. To address these challenges, a range of techniques have emerged, primarily in two areas: prompt construction, which aims to guide the model's behavior through carefully crafted instructions, and response generation, which involves mechanisms for improving how the model synthesizes information. This section explores those techniques, compares their strengths and limitations, and justifies the most suitable strategies for the development of an airline-specific QA chatbot.

Attributes for comparison

Comparison table

Proposed Answer Generation Strategy for the Airline QA Chatbot

In evaluating how to generate high-quality responses for the airline QA chatbot, our research focused on identifying techniques that not only improve factual accuracy and reduce hallucinations but also align with constraints of cost, development effort, and scalability. We reviewed a broad set of strategies grouped into two practical categories: prompt construction and response generation.

Recent literature and benchmark studies show that many state-of-the-art improvements depend on complex architectures, such as multi-stage reranking pipelines or fine-tuned generation models. While these methods can significantly improve faithfulness and contextual accuracy, they often require substantial engineering effort and rely on large, domain-specific models. This makes them impractical for early-stage deployment, where simplicity, speed, and cost-efficiency are critical.

Instead, we found strong support for prompt-based methods that leverage general-purpose, cost-effective LLMs like GPT-4o-mini. These techniques offer a high return on investment by enhancing model behavior through structured prompting without needing model retraining. Among the most promising are Chain-of-Verification, which prompts the model to internally validate its output before responding, and Self-Correction, which encourages detection of contradictions or unsupported claims. Approaches like ReAct and Self-Ask further improve reasoning by breaking complex queries into sub-steps or decisions. We also identified the effective use of Few-Shot prompting, especially when paired with Chain-of-Thought (CoT) reasoning, as a lightweight method to steer answer generation toward reliable, structured outputs.

Given the need for fast, cost-efficient, and transparent development in the first iteration of the airline QA chatbot, we recommend the following stepwise adoption of answer generation techniques:

1. Prompt Engineering Best Practices
   • Use clear, structured prompts with system-level instructions emphasizing accuracy, faithfulness, and domain knowledge preference over generic completions.
   • Incorporate user intent explicitly and guide the model to avoid speculation.
2. Chain-of-Verification (CoVe)
   • Start with inline self-verification prompts that ask the model to validate factual basis before finalizing an answer.
   • Future expansion: use multi-stage verification with RAG-based re-checks or document comparisons.
3. Prompt Tactics During Tuning
   • Integrate and test ReAct, CoT, Self-Ask, and other tactics as enhancements based on use-case needs.
   • Use A/B testing and evaluation benchmarks (described in the Evaluation section) to guide iteration.
4. Add Self-Correction Layer
   • Introduce a second-generation pass that prompts the LLM to detect contradictions or unsupported claims in its own response.
   • This step may be triggered selectively for high-stakes queries (e.g., cancellations, legal clauses).

Evaluation methods

Robust evaluation of a QA chatbot is essential to ensure that its responses are accurate, relevant, and trustworthy. A critical challenge in modern AI systems, particularly those using large language models (LLMs), is the phenomenon of hallucination, where the system generates fluent but factually incorrect or fabricated information. In high-stakes domains like aviation, such hallucinations can result in misleading information for passengers, regulatory issues, and ultimately, loss of user trust. Thus, evaluation must go beyond surface-level fluency and measure faithfulness, relevance, and robustness to user intent and noisy input.

Attributes for comparison

Evaluation Metrics

References for metrics:

• https://arxiv.org/pdf/2501.00269 (pag 2-3)
• https://arXiv.org/pdf/2309.01431v2 (pag 3-5)
• https://arxiv.org/pdf/2312.10997 (pag 12, 14)
• https://arxiv.org/pdf/2405.07437 (pag 9 - 12)

Comparison table

Relevant benchmarks

Benchmark Landscape: What the Research Reveals

During our investigation, we studied a wide range of evaluation methodologies and datasets, spanning from foundational benchmarks like Natural Questions to more advanced evaluations such as DO-RAG. Despite an exhaustive review of domain-specific resources, no benchmarks tailored to the airline industry were found. Most domain-specific evaluation datasets center around medicine, law, and finance. Examples include:

• MedHalt and MedHallu – for detecting hallucinations in medical QA.
• FinanceBench – evaluates open-book financial QA performance.
• Hallucination-Free? (Stanford University) – one of the few studies assessing commercial legal chatbots using manually curated questions and evaluations. While insightful, its manual process and proprietary data make it infeasible for reuse in a scalable airline chatbot benchmark.

Two comprehensive surveys, arXiv:2404.12041v3 and arXiv:2405.07437, helped map the evolving landscape of hallucination evaluation, scoring techniques, and annotator reliability. These reviews confirmed a strong trend: modern evaluation workflows increasingly rely on LLMs as annotators and scorers, primarily due to their adaptability, speed, and cost-effectiveness. These LLMs are used to assess correctness, detect hallucinations, score fluency, and measure alignment with user intent.

From these findings, we distilled a strategy to design an evaluation framework suitable for our airline-specific, RAG-based chatbot, emphasizing automated, scalable techniques while incorporating human-in-the-loop validation for critical subsets.

Evaluation Framework and Strategy for the Airline QA Chatbot

The selection of evaluation methods was driven by the need to assess both the accuracy of question answering and the presence of hallucinations in generated responses. Among the many options, we prioritized frameworks that offer a clear methodology for scoring faithfulness, annotating hallucinations, and integrating seamlessly into development pipelines. A key factor in the comparison was the availability of tools or guidance for dataset creation, particularly those suited for RAG-based systems. Since no existing benchmarks target the airline domain, the selection focused on transferable techniques and workflows that could be adapted to build our own domain-specific dataset and benchmark, rather than relying on out-of-the-box solutions.

We divided the evaluation pipeline into two main phases: Dataset Creation and Benchmark Execution.

1. Dataset Creation

A carefully constructed dataset is the foundation of any meaningful evaluation. Our process includes the following stages:

1.1 Data Collection

• Extract authoritative content from airline websites, policies, FAQs, and internal documentation. Sources are filtered for factual accuracy and categorized (e.g., baggage, check-in, cancellations, legal, claims, etc.).

1.2 Question Generation

• Use LLMs (e.g., GPT-4) to generate representative user questions for each category.
• Perform a human review to filter out ambiguous, unanswerable, or overlapping questions.

1.3 Answer Generation & Candidate Retrieval

• For each validated question, generate three candidate answers.
• Backtrack each answer to identify the source question that would most likely yield it using semantic similarity matching. This simulates document-based retrieval in RAG systems (inspired by RAGAS).

1.4 Human Annotation (First Phase)

• Select a subset of question-answer pairs for manual annotation. Annotators label:
  • Correctness: Is the answer factually accurate?
  • Hallucination: Are any unsupported claims made?
• Compare human judgment with LLM-generated content to assess human-LLM agreement, helping validate the use of LLMs as proxy annotators.

1.5 Dataset Expansion (Second Phase)

• Extend the dataset with more diverse QA pairs using semi-automated generation.
• Add non-relevant questions and ambiguous queries to evaluate negative rejection and noise robustness.
• Annotate with tools like FaithBench, RAGAS, and HaluEval 2.0 to assess faithfulness (factuality with respect to retrieved context), fluency, and truthfulness.

2. Benchmark Definition & Integration

With a curated dataset, the benchmark process includes two evolving phases:

2.1 Initial Benchmarking

• Integrate a flexible evaluation framework such as RAGAS, which allows granular scoring of faithfulness, context relevance, and hallucination.
• Additional options include:
  • ARES – for robust scoring with reference-free metrics.
  • DeepEval – for integration into CI/CD workflows and regression detection.
• Each QA pair is evaluated using:
  • Traditional metrics: BLEU, ROUGE, METEOR, BertScore.
  • LLM-based evaluators (e.g., GPT-4 or Claude) that assign scores (1–10) for criteria like fluency, correctness, and alignment with context.

2.2 Advanced Benchmarking

• Include domain-adapted evaluations for:
  • Hallucination rate
  • Faithfulness to retrieved context
  • User intent alignment
  • Handling of irrelevant or misleading queries
• Introduce stress tests for negative rejection (e.g., chatbot says “I don’t know” instead of hallucinating) and noise robustness (performance with adversarial or incomplete inputs).

Integration into CI/CD

To ensure ongoing reliability:

• The benchmark suite is run after every major change to the retrieval or generation components.
• A snapshot-based evaluation is included as part of the CI/CD pipeline.
• Thresholds are defined for acceptance, particularly for hallucination rate, negative response handling, and faithfulness score. Regression triggers alerts or rollback mechanisms.

Conclusion

Despite the absence of off-the-shelf airline-specific benchmarks, our investigation revealed transferrable strategies and adaptable toolkits from domains like medicine, finance, and law. By combining structured dataset creation, automated LLM-assisted scoring, and benchmark integration into development pipelines, we propose a scalable, domain-sensitive, and high-fidelity evaluation methodology tailored to the realities of an airline QA chatbot. This ensures that not only is the system performant, but it is also trustworthy, resilient, and grounded in facts.

Conclusions

Ensuring accurate and faithful responses in a QA chatbot is a multi-faceted challenge that requires a careful balance between model capabilities, infrastructure feasibility, and evaluation reliability. Our investigation has shown that prompt-based answer generation techniques, such as Chain-of-Verification, Self-Correction, and structured prompting, offer a practical and scalable way to enhance response quality without incurring the costs of fine-tuning or complex model pipelines. At the same time, in the absence of an airline-specific benchmark, we propose a custom evaluation flow grounded in methodologies from adjacent domains. By focusing on hallucination detection, faithfulness scoring, and dataset creation strategies, and by integrating frameworks like RAGAS or HaluEval 2.0, the proposed evaluation process can be embedded into CI/CD pipelines to maintain quality over time. Together, the selected techniques and evaluation methods form a solid foundation for building a trustworthy, airline-grade AI chatbot that aligns with both user expectations and enterprise constraints.