Unite.AI1d
Post-RAG Evolution: AI’s Journey from Information Retrieval to Real-Time Reasoning
For years, search engines and databases relied on essential keyword matching, often leading to fragmented and context-lacking ...
Mayo Clinic’s secret weapon against AI hallucinations: Reverse RAG in action
To tackle data-retrieval-based hallucinations in non-diagnostic use cases, Mayo Clinic has applied CURE reverse RAG paired ...
labs.sogeti25d
LLM FINE-TUNING: ENHANCING LLMS FOR SPECIALIZED TASKS
By training an LLM on a specialized dataset ... Unlike prompt engineering or retrieval-augmented generation (RAG)—which enhance responses dynamically—fine-tuning alters the model’s internal weights ...
unite1mon
Fremtiden for RAG-Augmented Image Generation
Rather, it superimposes new data against its pre-trained network, much like an LLM augmenting responses with ... from Google – can only access images from a local custom database. Excitement around ...
GitHub3mon
medical-chatbot-with-llm-and-rag
This project builds a medical Q&A chatbot using a Large Language Model (LLM) with Retrieval-Augmented Generation (RAG). Developed with LangChain and Docker, it employs Prompt Engineering and a vector ...
GitHub9mon
Build5Nines SharpVector - a lightweight, in-memory Text Vector Database for any C# / .NET Applications
Build5Nines.SharpVector is an in-memory vector database library designed ... AI solutions augmenting the LLM (Large Language Model) with the ability to load additional context data with the AI prompt ...