Use Cases
MCP with Claude: Basics, Quick Tutorial, and Top 10 MCP Servers
What Is Model Context Protocol? Model Context Protocol (MCP) is an open-source standard from Anthropic that allows AI models, including Claude, to securely interact with local data, external tools, and databases, such as GitHub, Google Drive, or Slack. It functions as a connector (“bridge”) for components like Claude Desktop or Claude Code, enabling capabilities like […]
MCP Compliance: Model Context Protocol in Regulated Industries
What Are Key Compliance Considerations for Model Context Protocol (MCP)? Model context protocol (MCP) compliance refers to the efforts to ensure an MCP server operates in accordance with regulatory, security, and data governance requirements. MCP servers allow AI models to interact with external resources, such as internal databases, applications, and APIs, by providing them with […]
MCP vs. A2A: Examples, Key Differences, and How to Choose
This is part of a series of articles about Model Context Protocol. Introducing MCP and A2A MCP (Model Context Protocol) and A2A (Agent-to-Agent Protocol) are both AI agent protocols that are considered complementary to each other. MCP focuses on an agent’s interaction with tools, while A2A focuses on collaboration between multiple agents. MCP allows an […]
MCP Security: Top 6 Risks and AI Security Best Practices
This is part of a series of articles about Model Context Protocol. What Is MCP Security? The Model Context Protocol (MCP), an open standard that allows AI agents to connect to and interact with external tools, databases, and services. MCP security involves managing risks like prompt injection and unauthorized access to credentials through its direct […]
Model Context Protocol: Principles, Use Cases, and Key Technologies
What Is Model Context Protocol (MCP)? Model Context Protocol (MCP) by Anthropic is an open specification proposed by Anthropic, which enables AI models, agents, and supporting infrastructure to share and manage context. MCP defines a set of message formats and APIs that formalize how context, which can include state, instructions, or data, is communicated between […]
Building with MCP: Anthropic Guidance and Code Execution in Claude
What Is the Model Context Protocol (MCP) by Anthropic? This is part of a series of articles about the Model Context Protocol. The model context protocol (MCP) is a framework introduced by Anthropic for its language models, such as Claude. MCP improves dynamic tool use by enabling language models to interact with code execution environments […]
How MCP Servers Work, Use Cases and Notable Examples
What Is an MCP Server? This is part of a series of articles about Model Context Protocol. MCP servers are applications that expose AI agents to tools and services through the standardized Model Context Protocol (MCP), acting as a bridge between AI models and external data or functionality. They allow AI models to use tools […]
Anthropic Claude API: A Practical Guide
Dive into the Claude API – learn to integrate Anthropic’s Claude models into your apps: discover how Claude and Obot can help you build agents, handle prompts, and scale real-world workflows.
Top 10 RAG Tutorials in 2024 + Bonus LangChain Tutorial
What Is Retrieval Augmented Generation (RAG)? Retrieval-augmented generation (RAG) combines large language models (LLMs) with external knowledge retrieval. Traditional LLMs generate responses based solely on pre-trained data. With RAG, the model can access updated and specific information at the time of inference, providing more accurate and context-rich responses. This method leverages repositories of external data, […]
Intelligent Automation: Pros/Cons, Use Cases & 5 Key Capabilities
What Is Intelligent Automation (IA)? Intelligent automation (IA) is the integration of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) to create systems that can perform both routine and complex tasks. These systems improve over time by learning from data and user interactions, leading to increased efficiency and decision-making capabilities. By using […]
RAG vs. LLM Fine-Tuning: 4 Key Differences and How to Choose
What Is RAG (Retrieval-Augmented Generation)? Retrieval-Augmented Generation (RAG) merges large language models (LLMs), typically based on the Transformer deep learning architecture, with retrieval systems to enhance the model’s output quality. RAG operates by fetching relevant information from large collections of texts (e.g., Wikipedia, a search engine index, or a proprietary dataset) and fuses this external […]
Understanding RAG: 6 Steps of Retrieval Augmented Generation (RAG)
What Is Retrieval Augmented Generation (RAG)? Retrieval Augmented Generation (RAG) is a machine learning technique that combines the power of retrieval-based methods with generative models. It is particularly used in Natural Language Processing (NLP) to enhance the capabilities of large language models (LLMs). RAG works by fetching relevant documents or data snippets in response to […]