Developer Glossary

Key terms in AI, blockchain, backend, DevOps, and frontend — explained clearly for developers.

AI & ML

Retrieval-Augmented Generation (RAG)

A technique that enhances LLM responses by retrieving relevant documents from an external knowledge base before generating an answer.

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AI & ML

GraphRAG

An advanced RAG approach that uses knowledge graphs to capture entity relationships, enabling multi-hop reasoning over structured data.

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AI & ML

Large Language Model (LLM)

A neural network trained on massive text datasets that can understand and generate human language, powering chatbots, code assistants, and content generation.

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AI & ML

Vector Embeddings

Numerical representations of text, images, or other data in high-dimensional space, where semantically similar items are positioned close together.

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AI & ML

Vector Database

A specialized database optimized for storing and querying high-dimensional vector embeddings using similarity search algorithms.

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AI & ML

Fine-Tuning

The process of further training a pre-trained model on a specific dataset to adapt it for a particular task or domain.

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AI & ML

Transformer

A neural network architecture that uses self-attention mechanisms to process sequential data in parallel, forming the foundation of modern LLMs.

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AI & ML

Prompt Engineering

The practice of crafting and optimizing input prompts to guide LLMs toward producing desired outputs, including techniques like few-shot learning and chain-of-thought.

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AI & ML

AI Agents

Autonomous AI systems that use LLMs to plan, reason, and execute multi-step tasks by calling tools, APIs, and other services.

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AI & ML

Function Calling

An LLM capability that allows the model to generate structured function calls with arguments, enabling it to interact with external tools and APIs.

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AI & ML

Semantic Search

A search approach that understands the meaning and intent behind queries, using vector embeddings to find relevant results beyond keyword matching.

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AI & ML

LoRA (Low-Rank Adaptation)

A parameter-efficient fine-tuning method that trains small adapter layers instead of modifying the entire model, reducing memory and compute requirements.

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AI & ML

Knowledge Graph

A structured representation of real-world entities and the relationships between them, stored as nodes and edges in a graph database.

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