--- title: Best GoPenAI posts — September 2024 | daily.dev description: The most upvoted GoPenAI posts from September 2024, curated by the daily.dev community. canonical: https://daily.dev/sources/gopenai/best-of/2024/09 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/sources/gopenai/best-of/2024/09 og:type: website og:site_name: daily.dev og:title: Best GoPenAI posts — September 2024 | daily.dev og:description: The most upvoted GoPenAI posts from September 2024, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of GoPenAI — September 2024 1. 1 [](https://daily.dev/posts/introduction-to-llm-agents-how-to-build-a-simple-reasoning-and-acting-agent-from-scratch-part-1--j0lvsx61i "Introduction to LLM Agents: How to Build a Simple Reasoning and Acting Agent from Scratch (Part 1)") Article GoPenAI · 2y Introduction to LLM Agents: How to Build a Simple Reasoning and Acting Agent from Scratch (Part 1) Learn the fundamental concepts of building AI agents by implementing a simple reasoning and acting agent. This guide uses Ollama to run large language models locally and demonstrates the agent's ability to understand user queries, leverage web searches, and provide responses. The post outlines setting up necessary dependencies, implementing core functionalities, and explains the workflow of agent interaction, making it an excellent starting point for AI agent development. 79 1 2. 2 [](https://daily.dev/posts/build-an-advanced-rag-app-query-routing-gamtxjmvx "Build an Advanced RAG App: Query Routing") Article GoPenAI · 2y Build an Advanced RAG App: Query Routing This post explores how to build an advanced RAG application using a technique called Query Routing. Query Routing enables the application to make decisions based on a user's query, selecting the most appropriate action from predefined choices such as retrieving context from multiple data sources, using different indexes, or performing a web search. Various types of Query Routers are discussed, including LLM Selector Router, LLM Function Calling Router, Semantic Router, and more. Example implementations demonstrate how to create Query Routers and enhance the decision-making capabilities of RAG applications. 71 3 3. 3 [](https://daily.dev/posts/prompt-engineering-basic-guide-ighlczdz1 "Prompt Engineering Basic Guide") Article GoPenAI · 2y Prompt Engineering Basic Guide Prompt engineering is crucial for optimizing interactions with large language models (LLMs). It involves designing prompts that include instructions, context, input data, and output indicators. Various types like zero-shot, few-shot, chain-of-thought, self-consistency, tree of thoughts, graph of thought, and forest of thoughts are used to enhance and direct LLM responses effectively. Understanding these techniques helps in improving the safety and capabilities of LLMs. 54 2 4. 4 [](https://daily.dev/posts/building-llm-agents-from-scratch-part-2-a-conversational-search-agent-with-ollama-2hw0vimjo "Building LLM Agents from Scratch (Part 2) : A Conversational Search Agent with Ollama") Article GoPenAI · 2y Building LLM Agents from Scratch (Part 2) : A Conversational Search Agent with Ollama Explore the advanced development of a conversational search agent using Ollama, Llama 3.1, Jina Embeddings, and ChromaDB. Learn how function calling enables the agent to interact with external web search tools for more accurate responses. Step-by-step instructions are provided for installing dependencies, creating a custom search tool, and setting up an agent system, complete with memory and tool execution capabilities. 35 5. 5 [](https://daily.dev/posts/rag-x-graph-rag-noe0pcp2v "RAG X: Graph RAG") Article GoPenAI · 2y RAG X: Graph RAG GraphRAG is a Retrieval-Augmented Generation system that uses graph-based knowledge representation to enhance the accuracy, context awareness, and explainability of responses to complex queries. It constructs a knowledge graph from input documents, utilizes graph traversal algorithms and language learning models (LLMs), and offers visualizations of information connections. This approach preserves context, enables intelligent information traversal, and provides visual explanations, making it especially effective for handling complex and long documents. 32 6. 6 [](https://daily.dev/posts/dynamic-routing-in-rag-directing-user-queries-to-the-right-vector-store-with-open-source-models-wi50bfyo3 "Dynamic Routing in RAG: Directing User Queries to the Right Vector Store with Open Source Models") Article GoPenAI · 2y Dynamic Routing in RAG: Directing User Queries to the Right Vector Store with Open Source Models Generative AI applications can be optimized by integrating a semantic routing mechanism in the Retrieval-Augmented Generation (RAG) framework. This involves analyzing user queries and directing them to the most relevant vector stores, enhancing both accuracy and efficiency. The post demonstrates implementing a semantic router using a Nomic embedding model and Llama 3.1 for embeddings, covering machine learning, computer science, and economics topics. Advanced techniques like Multi-query translation and HyDE further refine the process, ensuring users receive pertinent information from diverse sources. 31 7. 7 [](https://daily.dev/posts/mastering-llms-with-langchain-part-1-what-is-langchain--ulnyx860b "Mastering LLMs with LangChain: Part 1 (what is LangChain?)") Article GoPenAI · 2y Mastering LLMs with LangChain: Part 1 (what is LangChain?) LangChain is a flexible and easy-to-use framework designed to streamline the development of applications using large language models (LLMs). It features reusable building blocks like prompts, chains, and agents to simplify creating a variety of NLP applications, including chatbots, text summarizers, and code-generation tools. The series will cover setting up LangChain, interacting with LLMs, and building practical applications. 22 8. 8 [](https://daily.dev/posts/rag-ix-adaptive-retrieval-8atxjwnvs "RAG IX: Adaptive Retrieval") Article GoPenAI · 2y RAG IX: Adaptive Retrieval Adaptive Retrieval-Augmented Generation (RAG) systems improve information retrieval by tailoring strategies based on query types. By integrating language models (LLMs) into different stages of the retrieval process, these systems provide highly accurate, contextually relevant, and nuanced responses. Such systems handle a variety of queries, including factual, analytical, opinion-based, and contextual, thereby enhancing user experience across diverse information needs. 19 9. 9 [](https://daily.dev/posts/transformer-from-scratch-in-tf-part-1-embedding-and-positional-encoding-xwm4azavs "Transformer from Scratch in TF Part 1: Embedding and Positional Encoding") Article GoPenAI · 2y Transformer from Scratch in TF Part 1: Embedding and Positional Encoding This post, the first part of a series, explores how to build a Transformer model from scratch using TensorFlow 2, focusing on embedding and positional encoding. It covers text tokenization using TensorFlow's TextVectorization layer, transforming text into numerical formats, and embedding words into vectors for machine language comprehension. The post also explains positional encoding to incorporate sequence information into embedding outputs, essential for the Transformer architecture. Through code demonstrations and visualizations, key concepts are clarified. Future posts will explore the Scaled Dot-Product Attention mechanism, a pivotal component of Transformers. 15 [See all GoPenAI archives](/sources/gopenai/best-of) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@graph":[{"@type":"CollectionPage","@id":"https://daily.dev/sources/gopenai/best-of/2024/09#page","url":"https://daily.dev/sources/gopenai/best-of/2024/09","name":"Best GoPenAI Posts — September 2024","description":"The most upvoted GoPenAI posts from September 2024, curated by the daily.dev community.","isPartOf":{"@type":"WebSite","url":"https://daily.dev"}},{"@type":"ItemList","@id":"https://daily.dev/sources/gopenai/best-of/2024/09#items","numberOfItems":9,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://daily.dev/posts/introduction-to-llm-agents-how-to-build-a-simple-reasoning-and-acting-agent-from-scratch-part-1--j0lvsx61i","name":"Introduction to LLM Agents: How to Build a Simple Reasoning and Acting Agent from Scratch (Part 1)"},{"@type":"ListItem","position":2,"url":"https://daily.dev/posts/build-an-advanced-rag-app-query-routing-gamtxjmvx","name":"Build an Advanced RAG App: Query Routing"},{"@type":"ListItem","position":3,"url":"https://daily.dev/posts/prompt-engineering-basic-guide-ighlczdz1","name":"Prompt Engineering Basic Guide"},{"@type":"ListItem","position":4,"url":"https://daily.dev/posts/building-llm-agents-from-scratch-part-2-a-conversational-search-agent-with-ollama-2hw0vimjo","name":"Building LLM Agents from Scratch (Part 2) : A Conversational Search Agent with Ollama"},{"@type":"ListItem","position":5,"url":"https://daily.dev/posts/rag-x-graph-rag-noe0pcp2v","name":"RAG X: Graph RAG"},{"@type":"ListItem","position":6,"url":"https://daily.dev/posts/dynamic-routing-in-rag-directing-user-queries-to-the-right-vector-store-with-open-source-models-wi50bfyo3","name":"Dynamic Routing in RAG: Directing User Queries to the Right Vector Store with Open Source Models"},{"@type":"ListItem","position":7,"url":"https://daily.dev/posts/mastering-llms-with-langchain-part-1-what-is-langchain--ulnyx860b","name":"Mastering LLMs with LangChain: Part 1 (what is LangChain?)"},{"@type":"ListItem","position":8,"url":"https://daily.dev/posts/rag-ix-adaptive-retrieval-8atxjwnvs","name":"RAG IX: Adaptive Retrieval"},{"@type":"ListItem","position":9,"url":"https://daily.dev/posts/transformer-from-scratch-in-tf-part-1-embedding-and-positional-encoding-xwm4azavs","name":"Transformer from Scratch in TF Part 1: Embedding and Positional Encoding"}]},{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Sources","item":"https://daily.dev/sources"},{"@type":"ListItem","position":3,"name":"GoPenAI","item":"https://daily.dev/sources/gopenai"},{"@type":"ListItem","position":4,"name":"Best of","item":"https://daily.dev/sources/gopenai/best-of"},{"@type":"ListItem","position":5,"name":"September 2024"}]}]} ```