Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation (RAG) is an AI approach that optimizes the content generated by Large Language Models (LLMs). It utilizes a reliable knowledge base beyond its training data to create a response.

LLMs utilize the content they are training on, to understand the context and meanings of the words. They then use the intelligence they’ve gained to generate content.

Limitations of LLM training in Enterprise use cases:

1. Lack of Domain Knowledge: If the LLM was not trained on content from the domain, it may not understand the query accurately.
2. Hallucination: When the LLM does not have the answer, it can make up an answer that could be inaccurate and cause issues.
3. Expensive Retraining: Each time you need to teach the model something new, you need to retrain it, which can be expensive in terms of downtime, resources required, costs etc.

The RAG approach resolves these problems by providing the LLMs with grounded, contextual knowledge.

In RAG-based systems when a user posts a query:

1. The system uses natural language understanding (NLU) to understand the user’s query and transform it into a format that is easy for retrieval.
2. The transformed query is then used to locate content in the knowledge base that answers the query.
3. Once the relevant content (documents, videos, webpages) is found, the AI system natural language generation (NLG) part of the system summarizes the content and generates the response.

Why is RAG better than traditional content generation?

Since the AI system is generating content at runtime using content retrieved from the knowledge base: adding, removing, or updating the information does not impact the AI system and does not require retraining. The information is relevant, accurate, and domain specific as it’s based on the knowledge captured and curated by the enterprise.

However, it is important to note that RAGs assume that the knowledge captured and curated is correct and up to date. If your knowledge base has outdated and conflicting information or information gaps, the model will be susceptible to hallucinations and inaccurate responses.

Ensure you have a good strategy in place to manage your knowledge base and design your RAG-based system to include human intervention appropriately.