AI Answers & RAG Glossary: Essential Concepts for Developers and Teams

A system prompt is the hidden instruction that defines an AI’s role, behavior, tone, and constraints before any user interaction begins. It guides how the model interprets input and shapes every response it generates.

Yes, Gemini AI integrates with Google Workspace for enhanced productivity. Automatic reporting from Google Sheets & Analytics, email insights, and improved collaboration via Docs & Drive are possible.

RAG in AI (Retrieve, Aggregate, Generate) is a framework commonly used in advanced chatbots and question-answering systems. Retrieve fetches data, Aggregate organizes it, and Generate formulates human-like responses.

Prompt engineering is pivotal across various domains like data science and UX design. Crafting well-defined prompts enhances data quality, reduces bias, and boosts user engagement. Key strategies involve clarity, relevance, and consistency.

Agentic AI, a subset of AI, focuses on autonomous, goal-oriented decision-making using reinforcement learning. It adapts to dynamic environments, offering applications in robotics, finance, and healthcare.

Structured data is organized and easily searchable, used in databases like SQL; it's ideal for structured queries and business intelligence. Unstructured data lacks a defined format, found in emails or multimedia.

Agentic AI systems can autonomously make decisions and take actions to achieve goals, unlike traditional AI. Key features include autonomy, goal orientation, adaptability, and environment interaction.

User-knowledge ingestion enables AI to improve responsiveness by processing diverse user inputs like text and behavior, thereby personalizing experiences in apps like Netflix and Alexa.

Agentic Workflow Orchestration involves the design, management, and execution of work processes to improve efficiency and productivity. It automates the arrangement and coordination of complex computer systems.

A Multi-source Knowledge Workspace is a platform that aggregates, analyzes, and presents data from various sources in a single interface. It facilitates collaboration, data management, and drives informed decision-making within organizations.

Hybrid Search Architecture in Retrieval-Augmented Generation (RAG) models combine vector and metadata filtering. Initially, vector similarity search identifies relevant documents based on their vector representations. Subsequently, metadata filtering refi