Learn how Aitomatic and the AI Alliance enhance Q&A systems with RAG, fine-tuning, and iterative reasoning, providing precise, domain-specific answers for better performance.
This technical report was originally published on The AI Alliance blog.
In today’s fast-paced, information-driven world, professionals in various fields require instant, precise, and tailored answers to complex questions. Whether it's doctors looking for the latest treatment options or financial analysts seeking insights into market trends, the need for accurate, domain-specific information is critical. Unfortunately, current AI systems often fall short, leading to inefficiencies and missed opportunities.
To tackle this issue, we at Aitomatic, in collaboration with the fellow members of the AI Alliance tools working group, conducted an extensive study on best practices for advancing domain-specific Q&A using retrieval-augmented generation (RAG) techniques. The study’s findings, published at arXiv, offer valuable insights and recommendations for AI researchers and practitioners aiming to optimize Q&A AI in specialized domains.
The study explores techniques like domain-specific fine-tuning and iterative reasoning to boost the performance of RAG-based Q&A systems.
Fine-Tuning: This technique involves customizing the embedding model used in RAG’s retrieval step, the generative model used in RAG’s generation step, or both. Fine-tuning embedding models enables them to retrieve more relevant information by understanding domain-specific terminology and context. Fine-tuning generative models enhances their ability to produce accurate and contextually appropriate answers.
Iterative Reasoning: This technique leverages the Observe-Orient-Decide-Act (OODA) paradigm to refine AI’s answers through multiple steps, continuously improving accuracy and relevance. The loop involves:
This iterative process enables the AI to consider multiple perspectives, gather additional information, and adjust its answers, much like human problem-solving.
"Our research provides best practices for advancing domain-specific Q&A using retrieval-augmented generation, accelerating AI systems that understand specialized knowledge." – Zooey Nguyen, AI engineer & author from Aitomatic
To evaluate the effectiveness of fine-tuning and iterative reasoning, experiments were conducted using the FinanceBench dataset. This open-sourced subset contains 10,000 financial-analysis questions about publicly traded companies, based on SEC filings. Various Q&A system configurations were compared, including generic RAG, fine-tuned RAG, and RAG with OODA Reasoning. Performance was measured using several metrics, including retrieval quality and answer correctness.
The results showed that fine-tuning significantly improved retrieval accuracy and answer quality. Notably, fine-tuning the embedding model in RAG’s retrieval step led to higher accuracy gains compared to fine-tuning the generative model.
Additionally, integrating iterative reasoning with the OODA loop yielded the highest performance improvements. The generic RAG with OODA reasoning configuration outperformed even the fully fine-tuned RAG, highlighting the critical role of iterative reasoning in enhancing Q&A systems.
Our collaborative efforts aim to empower the AI community by providing structured analysis and best practices for developing domain-specific Q&A systems.
"The AI Alliance is creating an ecosystem for open innovation and collaboration in AI, unlocking its potential to benefit society," said Anthony Annunziata, Head of AI Open Innovation and the AI Alliance at IBM.
Our work as part of the AI Alliance on domain-specific Q&A best practices highlights the potential of open innovation and collaboration in advancing AI technologies. By fostering knowledge sharing and bringing together diverse talents, we are accelerating progress and empowering the AI community to develop cutting-edge solutions.
"By promoting open-source tools and collaborating on their development, we're empowering the AI community to create powerful, adaptable, and responsible AI systems," added Adam Pingel, IBM Head of Open Tools and Applications at the AI Alliance.
For more details on the study, read the full paper on arXiv.
This technical report was originally published on The AI Alliance blog.
In today’s fast-paced, information-driven world, professionals in various fields require instant, precise, and tailored answers to complex questions. Whether it's doctors looking for the latest treatment options or financial analysts seeking insights into market trends, the need for accurate, domain-specific information is critical. Unfortunately, current AI systems often fall short, leading to inefficiencies and missed opportunities.
To tackle this issue, we at Aitomatic, in collaboration with the fellow members of the AI Alliance tools working group, conducted an extensive study on best practices for advancing domain-specific Q&A using retrieval-augmented generation (RAG) techniques. The study’s findings, published at arXiv, offer valuable insights and recommendations for AI researchers and practitioners aiming to optimize Q&A AI in specialized domains.
The study explores techniques like domain-specific fine-tuning and iterative reasoning to boost the performance of RAG-based Q&A systems.
Fine-Tuning: This technique involves customizing the embedding model used in RAG’s retrieval step, the generative model used in RAG’s generation step, or both. Fine-tuning embedding models enables them to retrieve more relevant information by understanding domain-specific terminology and context. Fine-tuning generative models enhances their ability to produce accurate and contextually appropriate answers.
Iterative Reasoning: This technique leverages the Observe-Orient-Decide-Act (OODA) paradigm to refine AI’s answers through multiple steps, continuously improving accuracy and relevance. The loop involves:
This iterative process enables the AI to consider multiple perspectives, gather additional information, and adjust its answers, much like human problem-solving.
"Our research provides best practices for advancing domain-specific Q&A using retrieval-augmented generation, accelerating AI systems that understand specialized knowledge." – Zooey Nguyen, AI engineer & author from Aitomatic
To evaluate the effectiveness of fine-tuning and iterative reasoning, experiments were conducted using the FinanceBench dataset. This open-sourced subset contains 10,000 financial-analysis questions about publicly traded companies, based on SEC filings. Various Q&A system configurations were compared, including generic RAG, fine-tuned RAG, and RAG with OODA Reasoning. Performance was measured using several metrics, including retrieval quality and answer correctness.
The results showed that fine-tuning significantly improved retrieval accuracy and answer quality. Notably, fine-tuning the embedding model in RAG’s retrieval step led to higher accuracy gains compared to fine-tuning the generative model.
Additionally, integrating iterative reasoning with the OODA loop yielded the highest performance improvements. The generic RAG with OODA reasoning configuration outperformed even the fully fine-tuned RAG, highlighting the critical role of iterative reasoning in enhancing Q&A systems.
Our collaborative efforts aim to empower the AI community by providing structured analysis and best practices for developing domain-specific Q&A systems.
"The AI Alliance is creating an ecosystem for open innovation and collaboration in AI, unlocking its potential to benefit society," said Anthony Annunziata, Head of AI Open Innovation and the AI Alliance at IBM.
Our work as part of the AI Alliance on domain-specific Q&A best practices highlights the potential of open innovation and collaboration in advancing AI technologies. By fostering knowledge sharing and bringing together diverse talents, we are accelerating progress and empowering the AI community to develop cutting-edge solutions.
"By promoting open-source tools and collaborating on their development, we're empowering the AI community to create powerful, adaptable, and responsible AI systems," added Adam Pingel, IBM Head of Open Tools and Applications at the AI Alliance.
For more details on the study, read the full paper on arXiv.
