![Decoding LLM Hallucinations: A Deep Dive into Language Model Errors](https://assets.zilliz.com/large_June_14_Decoding_LLM_Hallucinations_A_Deep_Dive_into_Language_Model_Errors_6c3e600903.png)
Engineering
Decoding LLM Hallucinations: A Deep Dive into Language Model Errors
This post explores the concept of hallucinations and their potential triggers. Additionally, we introduced four practical methods for detecting hallucinations: self-evaluation, reference-based methods, uncertainty-based methods, and consistency-based detection.
![Advanced Retrieval Augmented Generation (RAG) Apps with LlamaIndex](https://assets.zilliz.com/large_June_04_Advanced_Retrieval_Augmented_Generation_apps_with_Llama_Index_1_b9733e4651.png)
Engineering
Advanced Retrieval Augmented Generation (RAG) Apps with LlamaIndex
Laurie’s presentation showcases basic and advanced application frameworks for RAG, which we can build with minimal lines of code using LlamaIndex. LlamaIndex also provides us with LlamaParse, which can help us index our data into our favorite vector databases like Milvus locally or on the cloud. It’s ultimately up to the user to choose what best fits their use cases. This presentation also showcased various RAG strategies that one can opt for to optimize Retrieval and Generation.
![Spring AI and Milvus: Using Milvus as a Spring AI Vector Store](https://assets.zilliz.com/large_Spring_AI_and_Milvus_Using_Milvus_as_a_Spring_AI_Vector_Store_297bf1d1d7.png)
Engineering
Spring AI and Milvus: Using Milvus as a Spring AI Vector Store
A comprehensive guide on how to use Milvus as a Spring AI vector store