AI Exam Flashcards: NLP, Agentic AI, RAG, and Multi-Agent Architectures

Vocabulary flashcards covering NLP fundamentals, agentic tools and reliability, RAG frameworks, multi-agent architectures, and AI benchmarking methods.

Natural Language Processing (NLP)

A field of AI that enables computers to understand and generate human language.

Bag-of-Words (BoW)

A model that converts text into numerical vectors based on word-frequency counts, while ignoring word order, grammar, and context.

Word2Vec

A model that uses a shallow neural network to learn dense word embeddings from surrounding words, capturing semantic and syntactic relationships mathematically.

Agentic AI

An AI paradigm where an LLM pursues goals by reasoning, observing, taking actions, using tools, and adapting to feedback.

Agency

The property that emerges in an AI agent when reasoning is combined with the ability to execute actions.

Local Tools

Tools in Agentic AI that operate internally using predefined rules, such as calculators or databases.

API-based Tools

Tools that connect agents to external services and real-time information, such as weather, financial, or ticketing systems.

Retries

A reliability mechanism that repeats failed calls after delays to address issues like network timeouts, expired API keys, rate limits, and server downtime.

Exponential Backoff

A retry strategy that progressively increases the waiting time between attempts to avoid system overload and excessive requests.

Fallbacks

A reliability mechanism where an agent switches to alternative tools when the primary tool fails.

Caching

The practice of storing previous results to reduce repeated requests and maintain system availability.

Traditional RAG

A retrieval architecture that follows a fixed pipeline: user query, document retrieval from a vector database, adding documents to context, and answer generation.

Agentic RAG

An advanced retrieval method where an agent dynamically decides if retrieval is needed, selects databases or APIs, formulates queries, and retries if results are poor.

Multi-Agent RAG

A retrieval framework where multiple specialized agents gather information from distinct sources (e.g., vector databases, Gmail, Slack) to produce a combined response.

Coordinator Agent

An agent in a Multi-Agent RAG system responsible for delegating searches to specialized agents and synthesizing their findings into one unified response.

Centralized (Supervisor) Multi-Agent Architecture

A multi-agent design where a single supervisor agent assigns tasks and coordinates specialists; it is easy to monitor but forms a single point of failure.

Decentralized (Peer-to-Peer) Architecture

A multi-agent setup where agents coordinate directly without a central leader, improving fault resilience but creating communication overhead.

Hierarchical Multi-Agent Architecture

An architecture where agents operate across defined management levels (such as strategic planning, coordination, and execution), which can encounter bottlenecks between tiers.

Federated Multi-Agent Architecture

An architecture where agents across separate organizations (e.g., banks, hospitals) share specific insights rather than raw data to preserve privacy and regulatory compliance.

Swarm Multi-Agent Architecture

A system where hundreds of lightweight agents explore problems concurrently via simple local rules, offering broad coverage but requiring a consolidator to manage and synthesize results.

Public AI Benchmarking

Evaluation that uses standardized datasets such as MMLU and HumanEval to establish general reasoning, coding, and baseline AI capabilities.

Custom AI Benchmarking

Evaluation tailored to domain-specific workflows, real-world performance, and edge cases to ensure agents perform reliably on their designated tasks.