mlsanityData to 14 Oct 2026

Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents

arXiv 2412.17942 ↗First posted 23 Dec 2024cs.AIcited by 1

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

We present a question-and-answer (Q&A) application designed to support the contract management process by leveraging combined information from contract documents (PDFs) and data retrieved from contract management systems (database). This data is processed by a large language model (LLM) to provide precise and relevant answers. The accuracy of these responses is further enhanced through the use of Retrieval-Augmented Generation (RAG), text-to-SQL techniques, and agents that dynamically orchestrate the workflow. These techniques eliminate the need to retrain the language model. Additionally, we employed Prompt Engineering to fine-tune the focus of responses. Our findings demonstrate that this multi-agent orchestration and combination of techniques significantly improve the relevance and accuracy of the answers, offering a promising direction for future information systems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 11 of its 24 references that are in the map. First posted on arXiv 23 Dec 2024.

Builds on

11 of 24 references.

References · 11

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  3. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
  4. 2023Augmented Language Models: a Surveycited by 403
  5. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  6. 2022A Survey on Retrieval-Augmented Text Generationcited by 158
  7. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 119
  8. 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Futurecited by 86
  9. 2024Speculative RAG: Enhancing Retrieval Augmented Generation through Draftingcited by 77
  10. 2023A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecturecited by 28
  11. 2023Querying Large Language Models with SQLcited by 14

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Synergy over Discrepancy: A Partition-Based Approach to Multi-Domain LLM Fine-Tuningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models
  2. 2025A Survey of Large Language Model Agents for Question Answering
  3. 2023On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering
  4. 2026Accurate Table Question Answering with Accessible LLMs
  5. 2024Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types
  6. 2025PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements
  7. 2026DataFactory: Collaborative Multi-Agent Framework for Advanced Table Question Answering
  8. 2024SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering