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Tags: rag* + llm* + retrieval-augmented generation*

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1. Getting Started with RAG

   This article explains Retrieval Augmented Generation (RAG), a method to reduce the risk of hallucinations in Large Language Models (LLMs) by limiting the context in which they generate answers. RAG is demonstrated using txtai, an open-source embeddings database for semantic search, LLM orchestration, and language model workflows.

   2024-06-22 Tags: rag, llm, hallucinations, txtai, embeddings database, semantic search, orchestration, text, github by klotz
2. Llama, Llama, Llama: 3 Simple Steps to Local RAG with Your Content

   This article guides you through the process of building a local RAG (Retrieval-Augmented Generation) system using Llama 3, Ollama for model management, and LlamaIndex as the RAG framework. The tutorial demonstrates how to get a basic local RAG system up and running with just a few lines of code.

   2024-06-21 Tags: llama 3, ollama, llamaindex, rag, localllama, llm by klotz
3. Prompting Command R

   Learn about how to prompt Command R: Understand the structured prompts used for RAG, formatting chat history and tool outputs, and changing sections of the prompt for different tasks.

   2024-06-19 Tags: command r, llm, prompt engineering, rag by klotz
4. RAG CLI

   A CLI tool for interacting with local or remote LLMs to retrieve information about files, execute queries, and perform other tasks in a Retrieval-Augmented Generation (RAG) fashion.

   2024-06-21 Tags: cli, rag, llm, llamaindex by klotz
5. llamaindex

   LlamaIndex comes with a built-in indexing feature, which allows developers to index large datasets efficiently. This makes it easier to search and retrieve information from these datasets, ultimately improving the overall performance of LLM-based applications.

   2024-06-18 Tags: llamaindex, llm, rag by klotz
6. Enhance Your RAG Application With Advanced SQL Vector Queries

   This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.

   2024-06-14 Tags: rag, sql, vector database, myscaledb, openai, langchain, hugging face, hackernews, api, llm by klotz
7. Unlocking the Power of Unstructured Data with RAG

   This article discusses the potential of unstructured data in software development and how it can be leveraged using Retrieval-Augmented Generation (RAG). The article highlights various types of unstructured data on GitHub, the value of unstructured data, and how RAG can be used to extract insights from this data. The article also explains how RAG works, its applications, and its benefits for developers and organizations.

   2024-06-13 Tags: github, blog, unstructured data, rag, llm by klotz
8. The Challenges of Retrieving Relevant Context for RAG

   Case study on measuring context relevance in retrieval-augmented generation systems using Ragas, TruLens, and DeepEval. Develop practical strategies to evaluate the accuracy and relevance of generated context.

   2024-06-11 Tags: natural language processing, rag, machine learning, llm by klotz
9. Accidental prompt injection against RAG applications

   Simon Willison explains an accidental prompt injection attack on RAG applications, caused by concatenating user questions with documentation fragments in a Retrieval Augmented Generation (RAG) system.

   2024-06-06 Tags: llm, prompt injection, rag, simon willison by klotz
10. Make room for RAG: How Gen AI's balance of power is shifting

    The technology of retrieval-augmented generation, or RAG, could be pivotal in shaping the battle between large language models.

    2024-06-04 Tags: retrieval-augmented generation, large language models by klotz

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