RAG Applications Explained
The emergence of LLMs like ChatGPT has sparked a surge in interest for creating chat interfaces that utilize personal data, known as retrieval augmented generation (RAG). Developers face the challenge of selecting the right data infrastructure to support these applications, weighing options between specialized vector databases and traditional databases like Postgres, which have recently added vector support. The popularity of Postgres highlights the ongoing evolution and adaptation within the AI landscape.In this clip
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Eye on AI
How to Build Smarter AI Applications with PostgreSQL
Related Questions
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data, as discussed in the episode Vector Databases and the Power of RAG and the clip Evolution of AI, as well as in the episode with Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?
What are the most recent developments and trends in AI, large language models (LLMs), retrieval-augmented generation (RAG), etc., as discussed in the episode Vector Databases and the Power of RAG and the clip Evolution of AI?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode Building LLM-Based Applications with Azure OpenAI with Jay Emery - 657 and the episode with Cohere co-founder Nick Frosst on building LLM apps for business?