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Perplexity AI-Powered Search with Retrieval-Augmented Generation
Large Language Models (LLMs) are a cornerstone of today’s AI landscape, but their performance can be erratic. At times, they deliver spot-on answers; at other times, they seem to spew random data, dredged up from their training material. While LLMs understand how words are statistically related, they don’t know their meanings.
What it RAG?
Retrieval-Augmented Generation (RAG) framework is designed to enhance the quality of responses generated by LLMs by grounding them in external sources.
These sources augment the model’s internal data, providing a richer, more accurate informational context.
RAG offers two principal advantages:
- Reliable Information Access: It ensures that LLMs have access to the most current and reliable information available, enhancing the accuracy of their outputs.
- Verifiable Claims: It allows for the verification of the model’s assertions, bolstering trust in the AI’s responses.
By basing LLM responses on an external, verifiable data set, the chances of the model relying solely on its internal parameters are minimized. This reduction in dependency on pre-trained data decreases the likelihood of “hallucinations” — instances where the model generates…
