Retrieval-Augmented Generation (RAG) is an NLP technique that enhances the accuracy and factual grounding of Large Language Models (LLMs). LLMs, with their massive parameter counts, excel at generating human-quality text but can struggle with factual accuracy and keeping their responses current. RAG bridges this gap by integrating information retrieval with LLM generation.
Here's how it works:
- Retrieval: When a user query arrives, RAG first searches an external knowledge base – like a curated dataset or internal company knowledge repository – for relevant information. This retrieval stage leverages information retrieval techniques to identify the most pertinent passages.
- Augmentation: The retrieved information is then presented to the LLM alongside the original user query. This "augmentation" step essentially primes the LLM with factual grounding for its response generation.
- Generation: Equipped with both the user's intent and relevant factual context, the LLM generates its response. This response can be tailored to various tasks, such as question answering, summarisation, or creative text formats.
RAG offers several advantages:
- Improved Accuracy: By grounding the LLM in factual data, RAG reduces the risk of outputs containing factual errors or biases present in the LLM's training data.
- Enhanced Trustworthiness: RAG allows for citing retrieved information as sources, similar to footnotes in research papers. This transparency builds trust in the LLM's responses.
- Reduced Training Burden: Instead of retraining the entire LLM for new information, RAG simply requires updating the knowledge base. This is a more efficient and cost-effective approach.
Overall, RAG represents a significant step forward for LLMs, enabling them to deliver more reliable and trustworthy outputs in real-world IT applications.
