Progress Software Corporation (NASDAQ:PRGS) is one of the most undervalued technology stocks to buy according to analysts. On September 29, Progress Software announced the early customer review and testing of the Progress OpenEdge MCP Connector for Advanced Business Language/ABL. The purpose-built integration is designed to accelerate development and modernization for users of the Progress OpenEdge platform by empowering them with intelligent automation, domain-specific coding assistance, and guided moder ...

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]