What is retrieval augmented generation?

As we recently covered on the Talbot West website, retrieval augmented generation (RAG) is a way to harness large language models (LLMs) to perform well in specialist applications.

RAG combines the power of LLMs with the precision of targeted data retrieval, offering a solution that's both powerful and practical for a wide range of enterprise applications.

RAG works its magic by integrating the following components:

1. A knowledge base of information specific to your organization

2. An LLM trained to reference your data when queried

This combination allows RAG to draw from both broad knowledge (via the LLM) and your company's specific information, resulting in responses that are more accurate, relevant, and tailored to your needs.

Why RAG outperforms standard LLMs

While LLMs such as GPT-4 and Claude Sonnet are impressive, they have limited applicability to enterprise settings:

1. Lack of specialized knowledge: They don't know the nuances of your industry, and don't have access to your company's proprietary data.
2. Potential for inaccuracies: Without specific context, LLMs may generate plausible but incorrect information.

RAG addresses these issues by grounding the AI's responses in your company's data, ensuring accuracy and relevance.

Not only your company's data: you can also import external data sources via API or other connector. For example, a financial services RAG could have real-time access to financial markets, as well as a comprehensive proprietary knowledge base of SOPs and custom instructions.

Use cases across industries

RAG's versatility makes it valuable across sectors. Here are a few examples:

1. Customer support: Access resolved tickets and internal specifications for rapid resolution.
2. Nonprofit: Accelerate grant proposals by giving the LLM access to your organization's mission, KPIs, brand objectives, and the requirements of the various donors.
3. Finance: Enhance fraud detection by cross-referencing transaction data with known fraud patterns.
4. Legal: Streamline document review by quickly retrieving relevant case law and precedents.
5. Manufacturing: Optimize supply chains by analyzing internal data and market trends.
6. Marketing: Generate on-brand marketing materials that address specific customer needs, initiatives, or product launches.

Implementing RAG in your enterprise

RAG implementations need to be done thoughtfully and with the following logic.

1. Start small: Begin with a pilot project in one department to demonstrate value.
2. Prioritize data quality: Ensure your knowledge base is accurate and well-organized (we offer document preprocessing services to get your docs in order).
3. Continuous monitoring: Regularly assess and refine your RAG system's performance.
4. Ethical considerations: Implement robust data protection and privacy measures.

The future is RAG

As businesses increasingly rely on AI for decision-making and customer interactions, RAG represents the next evolution in enterprise AI. By combining the broad capabilities of LLMs with the specific knowledge of your organization, RAG offers a powerful tool for improving efficiency, accuracy, and competitiveness.

About the author: Jacob Andra is the founder of Talbot West , a Utah-based AI advisory and implementation service. He loves talking about AI, content strategy, SEO, and branding.