Hailong Wang’s Post

In the age of advanced language models, engineering skills have become essential to effectively steer Large Language Model (LLM) behavior. Our latest technical blog delves into four sophisticated prompting techniques: Zero-Shot, Few-Shot, Chain-of-Thought, and Tree-of-Thought prompting. Tailored for architects and prompt engineers, the blog offers detailed explanations and real-world applications in healthcare and finance. Discover how these techniques can enhance your GenAI projects and drive innovation in your industry. Check out our blog now! Bikram Singh Sid Pandey Gaurav Malik Successive Tech #ai #healthcare #finance #innovation

New post on Netlight’s blog space where try to share insights at the edge of technology 👇 Netlight’s David Rochholz: There is a need for automated and effective solutions for extracting entities from large corpora of text. This blog post is meant for those who are familiar with GenAI technologies and the use case of entity extraction. #youcannotspellknowledgewithoutedge #edgex #creativitycompetencebusinesssensene

Empowering AI with GraphRAG AI is getting smarter and more precise, but what’s driving this evolution? GraphRAG — an innovative technology that combines the power of large language models and knowledge graphs — is revolutionising data processing and information retrieval. This powerful combination delivers precise, context-rich responses, significantly reducing errors and enhancing decision-making. Are you interested in how #GraphRAG is already being applied in healthcare, enhancing patient outcomes, and legal analytics, improving research efficiency? Or how it’s reshaping our understanding of AI's future? Wisecube has broken it all down in our latest article. Dive into the details and see where the future of AI is headed! ➡️ https://lnkd.in/ekC7Rauy #ArtificialIntelligence #MachineLearning #RAG #AI #KnowledgeGraph #DataScience

Fascinated by the future of AI and how technologies like GraphRAG are driving innovation! By combining large language models with knowledge graphs, GraphRAG is already reshaping industries like healthcare and legal analytics. It's exciting to see how this will continue to enhance decision-making and efficiency. 🚀 Check out this insightful article from Wisecube to learn more about the impact of GraphRAG on AI’s future.

🤖 Primer • AI Agents • http://agents.aman.ai - AI agents are autonomous systems that combine the decision-making and action-oriented capabilities of autonomous frameworks with the natural language processing and comprehension strengths of LLMs.  - The LLM serves as the “brain” within an agent, interpreting language, generating responses, and planning tasks, while the agent framework enables the execution of these tasks within a defined environment.  - Together, they allow agents to engage in goal-oriented workflows, where LLMs contribute strategic insights, problem-solving, and adaptability to achieve outcomes with minimal human intervention. - Here's the list of topics covered: • 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 - The "Agentic AI" Moment • 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 - Applications in Software Development • 𝐓𝐡𝐞 𝐀𝐠𝐞𝐧𝐭 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 - Agent Core (LLM) - Memory Modules - Tools - Planning Module • 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 - Reflection - Overview - Reflection Workflow: Step-by-Step Process - Beyond Self-Reflection: Integrating Additional Tools - Multi-Agent Framework for Enhanced Reflection - Function/Tool/API Calling - Tool Calling Examples: Web Search and Code Execution - Function Calling Datasets - Evaluation - Planning - Overview - Example • 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 - Background - Motivation - Implementation • Benchmarks • Common Use-cases of LLM Agents • 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬/𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 - AutoGen Studio - AutoGen - Swarm - CrewAI - Letta - Llama Stack - AutoRAG - Beam - AutoAgents - BabyAGI • 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 - Data Agent for Data Analysis - LLM-Powered API Agent for Task Execution • Build Your Own LLM Agent • Related Papers - Reflection - Tool Calling - Planning #artificialintelligence #genai

LangGraph! It's redefining how AI actors jam together in complex apps. Your AI elements can mingle and collaborate like never before. LangGraph is an extension to LangChain that takes collaboration to a whole new level. Here's what you need to know: - Installing LangGraph is as easy as waving the "pip install LangGraph" wand. - LangGraph adds cycles to your AI conversations, making chatbots more iterative and refined. - LangGraph plays well with LangChain chat models, treating chats like an elegantly scripted dialogue. - Cycles are crucial when you want your AI to ponder its actions and make smarter, more reliable decisions. It's not about going in circles; it's about soaring on feedback loops that make AI smarter and more reliable. With LangGraph, you can add a bit of 'roundabout intelligence' to your toolkit and craft AI narratives that circle back to brilliance. If you want to get started - visit: https://lnkd.in/eTFPhVQ4 #AICommunity #LangGraph #Innovation #MachineLearning #TechTrends #BuildSmart #CodeNewbie #ArtificialIntelligence #Developers #TechNews

The post discusses Retrieval-Augmented Generation (RAG), a technology that improves Large Language Models (LLMs) by addressing challenges such as scalability, integration, and data governance. RAG is explored in the context of enterprise applications, with scalability being a key aspect. The post also mentions the importance of a robust and efficient data management system in AI applications. https://lnkd.in/dwByKUj6

Deploying large language models (LLMs) and AI engines involves significant logistics beyond their creative applications. Semantic analytics applications, which aid in declarative optimization, ensure programs run efficiently in diverse environments. This mirrors the evolution of data handling practices from the 1970s. With rapid environmental changes, programmers must continuously manage and optimize these cognitive systems, akin to feeding a lion a better steak.

RAG Foundry RAG Foundry is a comprehensive framework that integrates data creation, training, inference, and evaluation into a single workflow. This integration facilitates the creation of data-augmented datasets for training and evaluating LLMs in RAG settings. Key Components Data Creation: Dataset Processing: The framework includes a processing module that creates datasets by persisting RAG interactions. This involves dataset loading, normalization, data aggregation, information retrieval, API integration, and template-based prompt creation. Consistent Format: The data is saved in a consistent, model-independent format, along with all relevant fields and metadata. Training: Parameter-Efficient Fine-Tuning (PEFT): The framework supports efficient training methods, allowing users to fine-tune models on the augmented datasets. Supervised Fine-Tuning: Users can train models using supervised fine-tuning techniques and push the trained models to platforms like Hugging Face Hub. Inference: Prediction Generation: The framework facilitates generating predictions using the augmented datasets with both trained and untrained LLMs. Evaluation: Metrics: The evaluation module supports a variety of metrics, including Exact Match (EM), F1, ROUGE, BERTScore, and custom metrics. These metrics can be applied locally (on each example) or globally (on the entire dataset). Comprehensive Assessment: The evaluation process assesses both retrieval accuracy and generative quality through a multi-faceted approach. Benefits Rapid Prototyping: The framework enables fast prototyping and experimentation with various RAG techniques and configurations. Improved Performance: By augmenting and fine-tuning models with diverse RAG configurations, the framework demonstrates consistent improvements across knowledge-intensive datasets. Open-Source: The code for RAG Foundry is released as open-source, encouraging collaboration and further development by the community. Applications Knowledge-Intensive Tasks: The framework is particularly useful for tasks that require access to up-to-date and accurate information, such as customer support, content creation, and research. The RAG Foundry framework represents a significant advancement in enhancing the capabilities of LLMs by integrating retrieval-augmented generation techniques, making it a valuable tool for various applications. Research Paper Link: https://lnkd.in/diWVHMZc #llm #genai #ai

🎯 RAG Evaluation: Don’t Let Customers Tell You First 🔍 The Pinecone article discusses the importance of evaluating Retrieval-Augmented Generation (RAG) systems, which enhance the accuracy and performance of generative AI applications by providing contextually relevant data. It highlights the need for robust evaluation frameworks to measure how well the retrieval component selects and ranks relevant documents. 📊 Key Highlights 🔹 Evaluation Metrics: → Binary Relevance Metrics: Precision@k, Recall@k, and F1@k for order-unaware and order-aware evaluations. → Graded Relevance Metrics: Discounted Cumulative Gain (DCG) and Normalized Discounted Cumulative Gain (NDCG) for measuring relevance on a spectrum. 🔹 Frameworks and Tools: → Arize: Focuses on real-time monitoring with Precision, Recall, and F1 scores. → ARES: Uses Mean Reciprocal Rank (MRR) and NDCG, ideal for dynamic environments. → RAGAS: Emphasizes Average Precision (AP) and Faithfulness, suitable for initial evaluations. → TraceLoop: Traces information flow and origin, ensuring factual consistency. → TruLens: Domain-specific optimization, focusing on precision and accuracy. 🔹 Practical Tips: → Setting Baselines: Establish benchmarks using metrics like F1 Score and NDCG. → Iterative Improvement: Regularly monitor and refine RAG systems. → Cost-Benefit Analysis: Balance performance goals with computational costs. Proper evaluation of RAG systems ensures they meet performance goals, enhancing the reliability and accuracy of AI applications. This process is crucial for maintaining high standards and improving user satisfaction. 👉 https://lnkd.in/dg-Q9wPe #LLM #RAG #RAGEvaluation #AIAccuracy #DataRetrieval #AIMetrics #AIQuality #PineconeAI