Understanding Retrieval-Augmented Generation (RAG)
Learn the basics of retrieval-augmented generation (RAG) and how it works.
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LLMs are limited by the data they are trained on and may not always have access to the most recent information. Additionally, a lack of access to external data leads to inaccurate results and hallucinations. Techniques such as prompt engineering and model fine-tuning are used to work around these limitations. Other techniques, such as sending additional context to the LLM to help it derive the right answer, are also available.
Each has its pros and cons. For example, prompt engineering (like one-shot prompting) is cost-effective but limited in scope. We can pass additional documents or information to LLM, but these techniques might be impacted by LLM token limitations and cost.
What is retrieval-augmented generation (RAG)?
Vector databases can store data in specialized form (vectors) that LLMs can access. RAG works hand-in-hand with vector databases. It aims to overcome LLM limitations by allowing them to dynamically retrieve relevant knowledge while generating responses. The idea is to have a component that can retrieve relevant ...