What is retrieval augmented generation?

Retrieval augmented generation (RAG) enhances the capabilities of large language models (LLMs) by combining them with external knowledge sources. This integration improves the accuracy and reliability of AI outputs. It consists of two main phases:

Phase 1: Retrieval 

• Understanding your request (prompt): The process begins with the input prompt or question provided by the user. The LLM processes this prompt to understand the context and information requirements.

• Targeted information retrieval: Based on the prompt, the LLM formulates a specific search query. This query is used to retrieve relevant information from connected external databases or knowledge bases. This step ensures that the response is grounded, or anchored, to verified, up-to-date, and relevant information, to enhance the accuracy and reliability of the output.

Phase 2: Generative 

• Enriched input: The retrieved information is fed back into the LLM, injecting a layer of factual data to supplement the LLM's internal knowledge.

• Enhanced response generation: With the enriched understanding provided by the retrieved information, the LLM generates a more informative and accurate response to the prompt. When generating the response, the system can include references or citations to these sources, providing transparency and allowing users to verify the information.

While the terms RAG and grounding are sometimes used interchangeably, it’s important to understand that grounding is actually a distinct step in the RAG process. Grounding ensures that the RAG generated responses are based on and anchored to accurate, up-to-date information retrieved from external knowledge sources.

Retrieval augmented generation (RAG) significantly enhances the accuracy and reliability of AI-generated responses by combining the strengths of large language models with verified external knowledge sources. 

This hybrid approach ensures that outputs are not only contextually relevant but also factually grounded. By pulling from trustworthy external sources, RAG also addresses certain challenges of LLMs, including accuracy, sparse data, access to updated information, and domain specificity.

This makes RAG highly valuable for applications requiring precise and trustworthy information, such as enterprise solutions, customer support, and content creation.

RAG offers several advantages for companies looking to leverage genAI effectively. Here's why RAG matters for businesses:

1. Mitigating risks and ensuring responsible AI: Companies have a responsibility to ensure their AI is reliable and trustworthy. RAG helps by grounding AI outputs in factual data, reducing the risk of misinformation and biased results. This is crucial for areas like marketing or finance, where inaccurate information can have serious consequences.

2. Real-time information: Businesses rely on up-to-date insights for effective decision-making. RAG empowers AI systems to access and process the latest information, enabling them to generate more comprehensive reports and faster analyses.

3. Improved interactions: Companies are increasingly using AI for chatbots and copilots. RAG allows these systems to access real-time product information or customer data, facilitating personalized and accurate responses to customer inquiries. This can significantly improve brand loyalty and user satisfaction.

4. Increased efficiency and productivity: RAG can automate tasks like information retrieval and report generation, freeing up employees for higher-level strategic thinking, streamlining workflows and boosting overall operational efficiency within a company.

5. Adaptability in a dynamic market:  Business needs can change rapidly. RAG allows AI systems to adapt to new information and market trends. This flexibility ensures AI remains relevant and valuable as the company operates in a constantly evolving environment.

RAG empowers companies to unlock the full potential of genAI by ensuring responsible, reliable, and adaptable AI solutions. This translates to better decision-making, improved user interactions, and a more efficient and competitive business overall.