Group 1 - Progress Software Corporation (NASDAQ:PRGS) is identified as one of the most undervalued technology stocks according to analysts [1] - On September 29, Progress Software announced the early customer review and testing of the Progress OpenEdge MCP Connector for Advanced Business Language/ABL [1] - The MCP Connector is designed to accelerate development and modernization for users of the Progress OpenEdge platform, providing intelligent automation and domain-specific coding assistance [2][3] Group 2 - The core functionality of the MCP Connector combines GenAI and Retrieval-Augmented Generation/RAG, offering precise coding guidance, refactoring support, and documentation generation aligned with OpenEdge standards [3] - Progress Software develops, deploys, and manages AI-powered applications and digital experiences both in the US and internationally [3]

Core Insights - Progress Software has announced the early customer review and testing of the Progress OpenEdge MCP Connector for ABL, which integrates Generative AI and Retrieval-Augmented Generation to enhance development and modernization for OpenEdge platform users [1][2][3] Group 1: Product Features and Benefits - The MCP Connector provides context-aware developer assistance tailored to OpenEdge Advanced Business Language (ABL), offering precise coding guidance, refactoring support, and documentation generation [2][3] - It integrates seamlessly with popular development environments like Visual Studio Code, enhancing developer productivity and allowing for flexible approaches to documentation [4][5] - The connector automates repetitive tasks such as boilerplate code generation and unit testing, improving code quality and compliance with organizational standards [7][8] Group 2: Strategic Value - The MCP Connector offers a future-ready foundation for organizations modernizing OpenEdge systems, reducing costs, mitigating risks, and enhancing developer productivity [6] - It supports real-time collaboration and aligns with business logic, enabling teams to deliver results faster and with greater confidence [6] - The connector aids in reducing technical debt through intelligent analysis and refactoring, accelerating time-to-value for modernization projects [8] Group 3: Customer Feedback and Market Potential - Early testing participants have expressed strong enthusiasm for the MCP Connector's potential to significantly boost developer productivity [4][5] - The integration of AI within the Progress development environment is seen as a key pillar for application modernization and refactoring strategies [5] - The early results indicate impressive improvements in developer productivity, showcasing the connector's promise in the evolving AI landscape [5]

General Overview - The document is a wrap-up message encouraging readers to reshare the content if they found it insightful [1] - It promotes the author's profile for daily tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) [1] Focus Area - The author, Avi Chawla, shares explanations on Model Context Protocol (MCP) with visuals [1]

RAG is 80% retrieval and 20% generation.So if RAG isn't working, most likely, it's a retrieval issue, which further originates from chunking and embedding.Contextualized chunk embedding models solve this.Let's dive in to learn more! https://t.co/vnQ5tAj1oe ...

Core Insights - The article discusses the rise of Retrieval-Augmented Generation (RAG) architecture as a key solution to address the limitations of large language models (LLMs) in providing real-time and accurate knowledge for enterprises [2][9]. Group 1: RAG Architecture Overview - RAG architecture enhances LLMs by integrating a retrieval mechanism that allows models to access up-to-date external knowledge, thus addressing issues of "hallucination" and knowledge timeliness [2][4]. - The typical RAG process involves retrieving relevant content from an enterprise knowledge base, dynamically constructing context prompts, and generating inference-based responses [3][4]. Group 2: Types of Companies in RAG Field - Companies in the RAG space include open-source tool providers like LangChain and LlamaIndex, startups focused on RAG platforms such as Vectara and Contextual AI, large cloud service providers like Microsoft Azure and AWS, and industry-specific application companies [5][6][7][8]. Group 3: Contextual AI's Innovations - Contextual AI, founded by researchers who pioneered RAG technology, aims to develop specialized AI agents capable of handling complex, knowledge-intensive tasks through its RAG2.0 technology [9][28]. - RAG2.0 emphasizes end-to-end optimization of retrieval and generation models, significantly improving system accuracy and response quality [26][28]. Group 4: Contextual AI's Product Workflow - Contextual AI allows enterprises to integrate their internal data sources into its platform, enabling real-time updates and access without manual data uploads [11]. - The platform has pre-deployed solutions for verticals like finance and law, allowing users to quickly build or utilize existing agents for specific tasks [13]. Group 5: Advantages of Contextual AI Solutions - Contextual AI's platform supports multi-modal retrieval and integrates structured and unstructured data from various sources, enhancing the retrieval process [18][21]. - The platform ensures result explainability and reliability by providing detailed source citations for generated answers, addressing enterprise needs for high-quality outputs [21]. Group 6: Team and Funding - The leadership team includes CEO Douwe Kiela, a pioneer of RAG technology, and CTO Amanpreet Singh, who has extensive experience in multi-modal model development [29]. - Contextual AI secured $20 million in seed funding in 2023 and $80 million in Series A funding in 2024, with a post-money valuation of approximately $609 million [30][31]. Group 7: Client Use Cases - HSBC collaborates with Contextual AI to develop an AI-driven research analysis assistant, while Qualcomm has signed a long-term contract to deploy custom models for precise answer retrieval from technical documents [32].

AI Tools & Resources - Recommends resharing insightful content related to DS, ML, LLMs, and RAGs [1] - Highlights 6 no-code LLMs, Agents, and RAG builder tools for AI engineers [1] - Focuses on open-source and production-grade AI tools [1] Author Information - Identifies Avi Chawla (@_avichawla) as a source of tutorials and insights [1]