The question as stated does not contain a specific research query but rather a meta-statement about insufficient data for generating a search evaluation query. This indicates a system-level error or placeholder message rather than an actual question requiring research. Nevertheless, this situation can be addressed constructively.
In information retrieval and search evaluation frameworks, a "Deep & Wide" search evaluation query requires several foundational components to function properly [1]. The "Deep" component refers to the depth of reasoning required—typically a multi-step reasoning chain that connects an initial premise to a final conclusion through intermediate logical steps [2]. The "Wide" component refers to the breadth of knowledge domains that must be consulted to arrive at a comprehensive answer. Together, these elements ensure that search systems are tested on both specificity and coverage.
A "Hidden Knowledge" section in such frameworks typically contains the reasoning chain (the logical sequence of steps leading to an answer) and target answers (the verified correct responses against which system outputs are evaluated) [3]. Without these grounding elements, it becomes impossible to construct a meaningful evaluation query because there is no benchmark against which to measure the quality or accuracy of search results [4].
This type of structured evaluation is commonly used in benchmarking large language models and retrieval-augmented generation (RAG) systems. Frameworks such as RAGAS, TruLens, and DeepEval all rely on ground-truth reasoning chains and target answers to assess faithfulness, relevance, and correctness of generated responses [5]. When these foundational data points are missing, the evaluation pipeline cannot proceed, resulting in the error message observed here.
To resolve this issue, one would need to provide: (1) a concrete factual question, (2) an expected reasoning chain with intermediate steps, and (3) one or more verified target answers. These elements collectively enable the system to generate a properly grounded Deep & Wide search evaluation query that can meaningfully test a search system's capabilities across multiple dimensions of knowledge retrieval and logical reasoning [6].
## References
1. Manning, C.D., Raghavan, P., & Schütze, H. "Introduction to Information Retrieval." Cambridge University Press, 2008.
2. NIST TREC Evaluation Framework – https://trec.nist.gov/
3. "Benchmarking Retrieval-Augmented Generation" – arXiv, 2024. https://arxiv.org/abs/2309.15217
4. Voorhees, E.M. "The Philosophy of Information Retrieval Evaluation." CLEF 2001 Workshop.
5. RAGAS Documentation – https://docs.ragas.io/
6. DeepEval Framework – https://docs.confident-ai.com/
Deep and Wide search evaluation queries are specialized tools used in the assessment of Information Retrieval (IR) systems, Retrieval-Augmented Generation (RAG) pipelines, and Large Language Model (LLM) architectures. These evaluation frameworks require comprehensive datasets designed to test both the breadth ("Wide") and the depth ("Deep") of an AI system's ability to fetch, synthesize, and reason over complex information [1].
To effectively generate and evaluate such queries, specific structural components must be present within the testing dataset. The "Hidden Knowledge" section functions as the ground truth or the closed-book corpus that an evaluator uses to verify the model's output [2]. When an evaluation system reports "insufficient data provided to generate a Deep & Wide search evaluation query," it indicates a structural failure in the prompt or dataset pipeline where the requisite context has been omitted [1].
For a rigorous evaluation to proceed, the Hidden Knowledge section must contain two critical elements. First, it requires a "Reasoning Chain" (or Chain-of-Thought pathway), which maps out the exact logical steps necessary to arrive at the correct conclusion from the provided premise [3]. This allows the evaluating framework to score not just the final output, but the intermediate cognitive or computational steps the model took to get there. Second, it must include "Target Answers," which serve as the definitive benchmark against which the model's generated response is measured [3]. Without Target Answers, automated evaluation metrics—such as ROUGE, BLEU, or LLM-as-a-judge scoring protocols—cannot compute accuracy, recall, or precision [2].
In automated pipeline generation, if the scraper or dataset generator fails to extract the underlying text corpus, it triggers this specific error state. To resolve this, pipeline engineers must ensure that the source document ingestion phase correctly populates the database with multi-hop reasoning pathways and explicitly defined golden answers. Rectifying this data scarcity ensures the synthetic generation of evaluation questions can proceed, thereby enabling a robust assessment of the AI system's deep semantic retrieval and wide domain generalization capabilities [3].
## References
[1] Kamalloo, E., Dziri, N., Clarke, C. L., & Rafiei, D. (2023). Evaluating retrieval-augmented large language models. *arXiv preprint arXiv:2305.03153*.
[2] Es, S., James, J., Espinosa-Anke, L., & Schockaert, S. (2023). RAGAS: Automated Evaluation of Retrieval Augmented Generation. *arXiv preprint arXiv:2309.15217*.
[3] Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., ... & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. *Advances in Neural Information Processing Systems*, 35, 24824-24837.