A
Adaptive Knowledge – AI's ability to modify its responses based on newly acquired data.
AI-Powered Assistants – Virtual assistants like ChatGPT, Siri, Alexa, which assist users dynamically.
Active Learning – AI technique where models learn continuously from user input or feedback.
Agent-Based AI – AI that can plan, execute, and adjust tasks autonomously.
API (Application Programming Interface) – A set of protocols allowing software components to communicate.
C
Contextual Awareness – AI’s ability to retain and apply past interactions to improve responses.
Continual Learning – A model’s ability to adapt and improve over time instead of being static.
Conversation Buffer Memory – A memory module that stores user interactions to provide continuity in conversations.
Cross-Source Validation – AI verifying information by comparing multiple sources for accuracy.
Chain-of-Thought (CoT) – AI reasoning technique where responses are generated step by step.
D
Dynamic Self-Improvement – AI’s capability to adjust its responses based on learning from interactions.
Document Summarization – AI technique where long documents are condensed into key points.
E
Embedding Models – AI representations of text, images, or data in numerical vector format for retrieval.
Extractive Summarization – AI method that selects key sentences or phrases from a document to create a summary.
F
FAISS (Facebook AI Similarity Search) – A vector database used to store and retrieve similar embeddings efficiently.
Feedback Loop – A mechanism where AI learns from user corrections or preferences to refine future outputs.
Fine-Tuning – The process of training an AI model on domain-specific data to enhance its performance.
G
Graph Database – A database designed to store and manage nodes, edges, and relationships (e.g., Neo4j).
Graph Embeddings – Vector representations of graph-based knowledge for AI learning.
H
Human-in-the-Loop (HITL) – A system where AI receives human feedback for continuous improvement.
Hybrid Retrieval – AI combining semantic, keyword, and structured search techniques for information retrieval.
I
Information Retrieval (IR) – AI’s ability to fetch relevant documents and data from external sources.
Inference Engine – AI component responsible for making logical decisions based on data.
K
Knowledge Expansion – The process where AI adds new verified information to its response generation pipeline.
Knowledge Graph – A structured database that stores relationships between entities for AI reasoning.
L
LAG (Learning-Augmented Generation) – AI technique that continuously updates itself based on feedback and new knowledge.
LLM (Large Language Model) – Advanced AI models like GPT-4, Claude, Llama that generate human-like text.
M
Memory-Based Retrieval – AI retrieving past user interactions to personalize and improve responses.
Model Drift – A situation where an AI model’s performance degrades over time due to outdated knowledge.
Multimodal AI – AI that processes and integrates text, images, audio, and video inputs.
N
Neural-Symbolic AI – AI combining machine learning (neural networks) with rule-based logic (symbolic AI).
Next-Gen RAG (RAG-2) – Enhanced Retrieval-Augmented Generation (RAG) with multi-step retrieval and summarization.
P
Personalized Embeddings – AI-generated representations that adapt to individual user preferences.
Prompt Engineering – The practice of designing structured inputs to get optimal AI responses.
Q
Query Expansion – AI technique where user queries are reformulated to improve retrieval accuracy.
Query Reformulation – Adjusting user input to better match stored knowledge in retrieval-based models.
R
RAG (Retrieval-Augmented Generation) – A method where AI retrieves external knowledge before generating responses.
RLHF (Reinforcement Learning from Human Feedback) – AI training method where models learn from user feedback.
Response Refinement – AI’s ability to improve outputs dynamically based on feedback and retrieval.
S
Self-Correction Loops – AI mechanisms that automatically refine responses based on continuous feedback.
Semantic Search – AI technique that understands intent rather than relying solely on keyword matches.
Structured Learning – AI method that incorporates organized datasets and defined rules into its training process.
Symbolic AI – AI approach based on logic, rules, and predefined knowledge representations.
T
Task Planning AI – AI that breaks down complex objectives into smaller, manageable steps.
Tool-Augmented Generation (TAG) – AI method where models call external APIs, functions, or tools to improve responses.
Token Limit – The maximum number of words or characters an AI model can process in a single interaction.
Transformer-Based AI – A type of deep learning model used in LLMs for processing and generating text.
V
Vector Database – A storage method used to store embeddings and perform similarity searches (e.g., FAISS, Pinecone).
Vector Search – AI method of finding similar documents using vectorized representations.
W
Weighted Retrieval Ranking – AI ranking system that prioritizes important information sources based on credibility.
Workflow Automation AI – AI capable of executing multi-step business or operational tasks autonomously.