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Dashboard design for Corvane

Love the clean, intuitive design! The smooth motion and transitions make the whole experience feel polished and engaging.

Building an LLM application that doesn't hallucinate? It’s all about the context. 🧠 I put together this high-level architecture breakdown of a Retrieval-Augmented Generation (RAG) pipeline to visualize how we can ground large language models in custom, domain-specific knowledge bases. While experimenting with these systems, here are the core stages I focus on to ensure accuracy: 1️⃣ Document Ingestion: Efficiently chunking and passing custom data (PDFs, APIs) through an embedding model into a vector database (like Pinecone or Chroma). 2️⃣ Semantic Search: Retrieving the 'Top-K' most relevant context chunks when a user submits a query. 3️⃣ Prompt Augmentation: Dynamically orchestrating the original query alongside the retrieved context. 4️⃣ Generation: Passing the enriched prompt to the LLM (like Qwen, Llama, or GPT) to generate a grounded response. 5️⃣ Continuous Evaluation: The most critical step. Using frameworks like RAGAS or TruLens to rigorously measure faithfulness, answer relevance, and context recall. Building the pipeline is only half the battle; evaluating its true effectiveness is what makes it production-ready. What frameworks are you currently preferring for your vector databases and RAG evaluation? Let’s discuss below! 👇 #GenerativeAI #MachineLearning #RAG #ArtificialIntelligence #LLM #DataScience