Retrieval Augmented Generation (RAG) Explained: Understanding Key Concepts
- Query the RAG anyway and let the LLM itself chose whether to use the the RAG context or its built in knowledge
- Query the RAG but only provide the result to the LLM if it meets some level of relevancy (ie embedding distance) to the question
- Run the LLM both on it's own and with the RAG response, use a heuristic (or another LLM) to pick the best answer
r/LocalLLaMA - Reddit
Retrieval-augmented generation (RAG) is a technique that enhances text generation by retrieving and incorporating external knowledge.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
Overall, RAG and RALMs overcome the limits of language models’ memory by grounding responses in external information.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
The explosion of generative AI made us pause and consider what was possible now that wasn’t a year ago. We tried many ideas which didn’t really click, eventually discovering the power of turning every feed and job posting into a springboard to:
- Get information faster , e.g. takeaways from a post or learn about the latest from a company.
- Connect the