iflow-mcp_cr21-rag-mcp

# Rise of Agentic AI and Birth of Model Context Protocol (MCP) ## The Evolution of AI: From Text Generation to Agentic Action ### 1. Early Limitations of LLMs Initial Large Language Models (LLMs) were groundbreaking in their ability to generate human-like text. However, they lacked precise instruction-following capabilities, often providing irrelevant or generalized responses. For example: - **User:** "Who is the president of the USA?" **LLM:** "The most influential person in the world." ### 2. Enhanced Instruction Following with SFT and RLHF To overcome these limitations, two critical methodologies emerged: - **Supervised Fine-Tuning (SFT)**: Models were explicitly trained on human-generated responses aligned with specific instructions. - **Reinforcement Learning from Human Feedback (RLHF)**: Models were further refined by human-ranked preferences, significantly enhancing relevance and coherence in generated responses. ### 3. Integrating Function Calling As users began to require precise, factual, and real-time information, LLMs integrated external tool invocation capabilities (function calling). For instance: - **User:** "What is the square root of 14.5232?" **LLM (Function Calling):** "The square root of 14.5232 is approximately 3.81." ### 4. Emergence of Agentic AI Demand for practical, real-world interactions led to the rise of Agentic AI—models capable of autonomously interacting with external applications and services to perform actions such as: - Scheduling calendar events - Sending messages - Conducting online transactions However, integrating with multiple diverse applications faced significant challenges, including: - Diverse technology stacks - Inconsistent API structures - Complex authentication requirements - Scalability issues with manual integrations ### 5. 📜 Birth of Model Context Protocol (MCP) As AI assistants became more capable, they remained trapped by **information silos** — isolated from real-world data in business tool…