DEV·1yLlama 3.3 vs OpenAI O1
Llama 3.3 and OpenAI O1 are two advanced AI models offering enhanced reasoning, scalability, and versatile applications. Llama 3.3 stands out with its open-source flexibility and cost-effective solution, while OpenAI O1 offers a user-friendly API and robust security. Apidog is recommended for integrating these AI models, simplifying API development with its intuitive interface.
ITNEXT·1yHow to Use an LLM-Powered Boilerplate for Building Your Own Node.js API
Discover how an enhanced Node.js API boilerplate utilizes LLM Codegen to generate module code based on text descriptions. This feature automates the creation of E2E tests, database migrations, and business logic. The boilerplate supports OpenAI and Claude LLM clients and adheres to vertical slicing architecture and Clean Code principles, ensuring the generated code is clean and maintainable.
Daily Dose of Data Science | Avi Chawla | Substack·1yRAG vs Agentic RAG
Agentic RAG systems introduce dynamic, adaptable behaviors into the traditional RAG workflow. Unlike traditional RAG, which retrieves and generates once, agentic RAGs iteratively refine queries and context, adapting based on the problem's complexity. This makes them more effective for complex queries and problem-solving. The open-source tool Opik by CometML supports the evaluation, testing, and monitoring of LLM applications from development to production, offering features like logging traces and detecting hallucinations.
Towards Data Science·1yHow to Build a Graph RAG App
Learn how to build a Graph RAG (Retrieval-Augmented Generation) app that uses knowledge graphs and large language models to retrieve, filter, and summarize medical journal articles. The app incorporates vector databases for initial searches and structured knowledge graph metadata for filtering and organization, leveraging the MeSH controlled vocabulary to ensure relevant results. This approach enhances accuracy, explainability, and domain-specific knowledge retrieval, applicable to various fields beyond medicine.
DevelopSense·1yVoldemort Syndrome
Since June 2023, James Bach and another researcher have been studying 'syndromes' related to AI chatbots, specifically those based on LLMs and GPTs like ChatGPT. Recently, they identified 'Voldemort Syndrome,' where the chatbot censors specific names, such as 'David Mayer.' The phenomenon suggests both input and output filtering are being used to restrict content. The post highlights the importance of interactive testing and understanding technology to identify issues without requiring deep technical knowledge.
Daily Dose of Data Science | Avi Chawla | Substack·1yBuilding a RAG app using Llama-3.3
Meta released Llama-3.3, and this post provides a hands-on demo for building a RAG app using it. The app allows users to interact with a document via chat. It uses LlamaIndex for orchestration, Qdrant for a self-hosted vector database, and Ollama for serving Llama-3.3 locally. The implementation steps include loading and parsing a knowledge base, creating embeddings, indexing and storing them, defining a custom prompt template, and setting up a query engine.
bycloud·1yI Read The Top 10 AI Research Papers of 2024
The post highlights the top 10 most cited AI research papers of 2024, reflecting significant progress and key trends in the AI field. It notes the rapid increase in the number of AI papers on the Archive platform and describes notable papers such as the Deep Seek Coder, Gemini 1.5 by Google, and Llama 3 by Meta. The post emphasizes the importance of citations in gauging the impact and relevance of these research papers and includes detailed descriptions of the contributions and innovations introduced by each paper.
Daily Dose of Data Science | Avi Chawla | Substack·1y[Hands-on] Tool calling in LLMs
Tool calling allows language models to perform specific tasks by invoking external tools or APIs. The process involves recognizing when an external tool is needed, invoking the tool, and integrating its output into the model's response. This enhances the flexibility and capability of LLMs. A demo is provided to build a stock price retrieval assistant using the yfinance library.
Medium·1yThe Death of the Stubborn Developer
The post discusses the significant changes in software development due to the rise of large language models (LLMs) and chat-oriented programming (CHOP). It argues that these AI tools can handle many routine tasks, leaving developers with more complex planning and coordination roles. This shift affects junior developers' growth and requires all developers to adapt to new programming modalities. The post also debates the possible future emergence of autonomous agents and emphasizes the importance of embracing CHOP to stay relevant.
Machine Learning Mastery·1yBuilding a Graph RAG System: A Step-by-Step Approach
Graph RAG is gaining popularity for its ability to organize retrieved data as a graph, connecting documents through nodes and edges to provide comprehensive and insightful responses. This method addresses the limitations of traditional Retrieval-Augmented Generation (RAG) systems, which often fail to connect fragmented information across multiple documents. The post details the step-by-step implementation of Graph RAG using LlamaIndex, including key processes like breaking down documents into text chunks, identifying nodes and edges, summarizing elements, and building communities for more effective data reasoning and responses.
portkey·1yExploring prompt engineering techniques for effective AI outputs
Prompt engineering has become a specialized skill essential for optimizing AI outputs. Various techniques such as zero-shot, few-shot, chain-of-thought, instruction-based, and role-based prompting improve task performance by structuring prompts precisely. Dynamic optimization, automation, and multi-prompt fusion offer scalable solutions, while meta prompting turns models into prompt engineers, enhancing prompt quality. Advanced methods like graph prompting and generated knowledge prompting address complex, structured tasks. Efficient and context-rich prompts are key to harnessing large language models' full potential.
Towards AI·1yLlm Fine Tuning Guide: Do You Need It and How to Do It
Fine-tuning a Large Language Model (LLM) is often unnecessary for many commercial applications, but it can be useful for tasks requiring specific chat formats, domain knowledge, or cost-effective, specialized tasks. Fine-tuning involves data preparation, including deduplication and removal of personal information, and can be done using techniques like LoRa (Low-Rank Adaptation) or QLoRA. Using reinforcement learning with human feedback (RLHF) or direct preference optimization (DPO) can align models with human preferences. For fine-tuning and hosting, cloud platforms like AWS SageMaker and collaborative tools like HuggingFace are recommended.
Cloudflare·1yHi Claude, build an MCP server on Cloudflare Workers
Anthropic announced a new way for AI applications to interact with services using Model Context Protocol (MCP). The post provides a guide on how to build an MCP server using Cloudflare Workers, making it easy to deploy services and applications that AI assistants like Claude can interact with. By leveraging Cloudflare Workers, developers can quickly set up MCP servers without dealing with complex configurations and schemas. The provided examples illustrate functionalities like generating images and sending messages seamlessly through AI integrations.
freeCodeCamp·1yHow to Run Open Source LLMs on Your Own Computer Using Ollama
Running open-source large language models (LLMs) on your computer ensures data privacy, cost savings, and customization. This guide discusses the prerequisites, differences between cloud-based and self-hosted AI, and provides a step-by-step tutorial on using Ollama to manage and run LLMs locally. Fine-tuning models for specific tasks and the potential benefits of self-hosting, such as enhanced data security and reduced latency, are also covered.
LangChain·1yLangChain State of AI 2024 Report
In 2024, AI application development saw significant shifts with a rise in open-source model adoption and a move towards AI agent applications. OpenAI remains the top LLM provider, while Ollama and Groq have gained traction. There’s notable growth in the use of multi-step and agentic workflows, with increased complexity in tasks and efficient performance. Python continues to dominate, though JavaScript usage is rising. Developers are placing greater emphasis on evaluating and iterating LLM responses using both automated and human feedback.
Daily Dose of Data Science | Avi Chawla | Substack·1y[Hands-on] Building A Multi-agent News Generator
Build a multi-agent news generator using Cohere’s Command R7B model and CrewAI for multi-agent orchestration. The app searches the web for a user query, consolidates the results, and converts them into a well-crafted news article. The implementation involves setting up a research analyst agent for web searches and a content writer agent for producing polished articles. Find the complete code and instructions on GitHub.
Towards Data Science·1yHow to Build a General-Purpose LLM Agent
Learn how to build a general-purpose LLM agent with a step-by-step guide that covers model selection, designing control logic, core instructions, tool definition, memory handling, parsing raw output, and orchestrating steps. Starting with a single-agent setup is ideal for prototyping before scaling to more complex systems.
Daily Dose of Data Science | Avi Chawla | Substack·1yA crash course on RAG systems—Part 7
Part 7 of the RAG crash course focuses on building graph RAG systems using a graph database to store entities and relationships. It highlights the advantages of structured data for LLMs and includes implementation details suitable for beginners. The series covers foundational aspects, evaluation, optimization, and multimodal techniques for RAG systems. Understanding RAG systems can help reduce costs, drive revenue, and scale ML models effectively.
Community Picks·1yevilsocket/nerve: Create LLM agents without writing code.
Nerve is a tool that allows users to create stateful agents with any language model (LLM) without writing code. It supports automated problem-solving, user-defined agents via YAML templates, universal tool calling, and compatibility with various LLM providers. Agents can perform tasks autonomously by planning, recalling memories, and updating information dynamically. Integration is provided for platforms like OpenAI, Huggingface, and Nvidia NIM, among others.
Google Cloud·1yRAG systems: Best practices to master evaluation for accurate and reliable AI.
Retrieval-augmented generation (RAG) enhances large language models by connecting them to dynamic and specialized data, but its implementation can be challenging. Thorough evaluation is crucial to avoid 'silent failures' that undermine system reliability. Best practices include establishing a rigorous, automated testing framework, selecting appropriate evaluation metrics, and combining quantitative and qualitative testing. Tools like Ragas and Google Cloud's Vertex AI Gen AI evaluation service can assist in optimizing RAG systems.
portkey·1yBuild vs Buy - LLM Gateways
Deciding whether to build or buy an LLM Gateway is crucial for managing and optimizing AI models within enterprises. Building in-house allows for extensive customization and control but requires significant resources and expertise. Conversely, purchasing an off-the-shelf solution offers quick deployment, built-in compliance, and ongoing support, often proving more cost-effective. The best choice depends on the organization's specific needs, resources, and strategic goals.
Hacker News·1yBuilding effective agents
Building effective agents for large language models (LLMs) involves using simple, composable patterns rather than complex frameworks. Successful implementation requires understanding the distinction between workflows and agents, and choosing the appropriate method based on the task's complexity and flexibility needs. While various frameworks like LangGraph and Amazon Bedrock can simplify the development process, it's crucial to understand the underlying code and avoid unnecessary complexity. Practical applications include customer support and coding agents, emphasizing the importance of simplicity, transparency, and well-documented interfaces.
Data Science Central·1yHow to Analyze SQL with LLMs Without Database Access?
SQL analysis is essential for extracting insights from data, but it can be challenging for non-technical users. Integrating SQL databases with Large Language Models (LLMs) allows users to pose questions in plain language and receive SQL queries as responses. However, this approach raises data security and privacy concerns. To safely leverage LLMs, businesses can use sandbox environments, unconnected query translators, and architecture that hides real data. These methods ensure secure and efficient data analysis while avoiding direct access to live databases.
DEV·1yBuild a Competitive Intelligence Tool Powered by AI
Integrating AI-powered Competitive Intelligence (CI) tools can help businesses stay ahead by tracking competitor activities, market shifts, and customer behavior in real time. These tools enable proactive adaptation, informed decision-making, and uncovering new opportunities. The post explains how to build a CI tool using Python, LangChain, Ollama, BrightData, Selenium, and Streamlit. The tutorial covers setting up the Python environment, creating a Streamlit UI, web scraping with Selenium, and parsing data with Ollama LLM. The tool aids in product development, marketing, sales outreach, and market awareness.
Daily Dose of Data Science | Avi Chawla | Substack·1y[Hands-on] RAG over Excel Sheets
Learn how to build a Retrieval-Augmented Generation (RAG) application using Excel sheets with Docling and Llama-3.2, utilizing layout analysis, table structure recognition models, and local LLM run. The detailed step-by-step process demonstrates parsing files, setting up models, embedding data, creating an index, and querying the data, with seamless integration into a Streamlit interface. Explore further advanced techniques in the upcoming RAG crash course.