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HFEPX · Eval paper review

RAG Strategies for Natural Language-Based SQL Query and REST API Call Generation

Tim Schlippe, Simon Martin, Michael Marketsmüller

Published

Feb 6, 2026

Citations

0

Trust level

Low

Usefulness score

25/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Domain Experts

Signals refreshed

Oct 17, 2026

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Tags

Automatic Metrics General Tool Use

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
25/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Enterprise software systems commonly expose functionality through relational databases and REST APIs, requiring users to understand schemas, endpoints, and parameters. Retrieval-augmented generation (RAG) can support natural language interfaces by grounding large language models (LLMs) in enterprise-specific documentation. We evaluate three training-free RAG configurations - Standard RAG, Relevance Filtering RAG, and Iterative Retrieval RAG - for SQL query generation, REST API call generation, and a combined task requiring routing between both operation types. Using SAP Transactional Banking, we constructed an expert-reviewed dataset with mock execution screening and compared the configurations under database-only, API-only, and mixed-documentation settings. Without retrieval, exact match accuracy is 0% across all tasks, whereas retrieval achieves mock execution success of up to 79.30% and component match accuracy of up to 78.86%. In the combined task, Iterative Retrieval RAG achieves higher exact match accuracy than Standard RAG (10.29% vs. 7.45%), with similar mock execution success (68.93% vs. 67.10%). However, for REST API call generation under mixed documentation, Iterative Retrieval RAG achieves lower mock execution success than Standard RAG (50.53% vs. 75.79%). Overall, retrieval-control strategies show task- and metric-dependent behavior under mixed-documentation settings.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Enterprise software systems commonly expose functionality through relational databases and REST APIs, requiring users to understand schemas, endpoints, and parameters."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Enterprise software systems commonly expose functionality through relational databases and REST APIs, requiring users to understand schemas, endpoints, and parameters."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Enterprise software systems commonly expose functionality through relational databases and REST APIs, requiring users to understand schemas, endpoints, and parameters."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Enterprise software systems commonly expose functionality through relational databases and REST APIs, requiring users to understand schemas, endpoints, and parameters."

Reported Metrics

partial

Accuracy, Exact match, Relevance

Useful for evaluation criteria comparison.

"We evaluate three training-free RAG configurations - Standard RAG, Relevance Filtering RAG, and Iterative Retrieval RAG - for SQL query generation, REST API call generation, and a combined task requiring routing between both operation types."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"Using SAP Transactional Banking, we constructed an expert-reviewed dataset with mock execution screening and compared the configurations under database-only, API-only, and mixed-documentation settings."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

accuracyexact matchrelevance
Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Domain Experts
Expertise required
General
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
Tool Use
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Enterprise software systems commonly expose functionality through relational databases and REST APIs, requiring users to understand schemas, endpoints, and parameters.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Enterprise software systems commonly expose functionality through relational databases and REST APIs, requiring users to understand schemas, endpoints, and parameters.
  • Retrieval-augmented generation (RAG) can support natural language interfaces by grounding large language models (LLMs) in enterprise-specific documentation.
  • We evaluate three training-free RAG configurations - Standard RAG, Relevance Filtering RAG, and Iterative Retrieval RAG - for SQL query generation, REST API call generation, and a combined task requiring routing between both operation types.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics, Tool-use evaluation) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Expert verification evaluation Accuracy

Contribution summary

  • We evaluate three training-free RAG configurations - Standard RAG, Relevance Filtering RAG, and Iterative Retrieval RAG - for SQL query generation, REST API call generation, and a combined task requiring routing between both operation…
  • Without retrieval, exact match accuracy is 0% across all tasks, whereas retrieval achieves mock execution success of up to 79.30% and component match accuracy of up to 78.86%.
  • In the combined task, Iterative Retrieval RAG achieves higher exact match accuracy than Standard RAG (10.29% vs.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    Detected: accuracy, exact match, relevance