Tags: retrieval augmented generation* + large language models*

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  1. A guide on implementing prompt engineering patterns to make RAG implementations more effective and efficient, covering patterns like Direct Retrieval, Chain of Thought, Context Enrichment, Instruction-Tuning, and more.

    2025-02-27 Tags: , , by klotz
  2. The article explains six essential strategies for customizing Large Language Models (LLMs) to better meet specific business needs or domain requirements. These strategies include Prompt Engineering, Decoding and Sampling Strategy, Retrieval Augmented Generation (RAG), Agent, Fine-Tuning, and Reinforcement Learning from Human Feedback (RLHF). Each strategy is described with its benefits, limitations, and implementation approaches to align LLMs with specific objectives.

    2025-02-25 Tags: , , , , , by klotz
  3. This article explores the use of Google's NotebookLM (NLM) as a tool for research, particularly in analyzing the impact of the Aswan High Dam on schistosomiasis in Egypt. The author details how NLM can be used to create a research assistant-like experience, allowing users to 'have a conversation' with uploaded content to gain insights and answers from the material.

  4. A simple project demonstrating Retrieval Augmented Generation (RAG) using SQLite, sqlite-vec, and OpenAI. It embeds text files, stores them in a SQLite database, and retrieves relevant documents using vector search. The project features lightweight single-file SQLite databases, vector search capabilities, and OpenAI integration for embeddings and chat responses.

  5. Learn how to use Okta FGA to secure your LangChain RAG agent in Python.

    2025-02-14 Tags: , , , , by klotz
  6. This article provides a step-by-step guide to creating an AI-powered English tutor using Retrieval-Augmented Generation (RAG). It integrates a vector database (ChromaDB) for storing and retrieving relevant English language learning materials and Groq API for generating structured and engaging lessons. The tutorial covers installing necessary libraries, setting up the environment, defining a vector database class, implementing AI lesson generation, and combining vector retrieval with AI generation.

  7. Llama Stack v0.1.0 introduces a stable API release enabling developers to build RAG applications and agents, integrate with various tools, and use telemetry for monitoring and evaluation. This release provides a comprehensive interface, rich provider ecosystem, and multiple developer interfaces, along with sample applications for Python, iOS, and Android.

    2025-01-25 Tags: , , , , , by klotz
  8. Discover how to run AI models locally with ease using tools like Msty, which simplifies the process of setting up, running, and managing local AI models on various operating systems.

    2025-01-08 Tags: , , , , by klotz
  9. An exploration of Retrieval-Augmented Generation (RAG) using Langchain and LlamaIndex, explaining how these tools can enhance Large Language Models (LLMs) by combining retrieval and generation techniques.

    2025-01-04 Tags: , , , by klotz
  10. LLM-powered bookmark search engine that allows you to search from your local browser bookmarks using natural language.

    2025-01-01 Tags: , , , , by klotz

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