RAG on English AI Terms Dictionary

LlamaIndex

Sat, 18 Jul 2026 10:05:29 +0000

Definition

Originally known as GPT Index, LlamaIndex is a powerful data framework that enables LLMs to ingest and interact with structured and unstructured data. It provides tools for indexing, querying, and managing data pipelines, making it easier to build applications that leverage private or domain-specific information. By integrating seamlessly with various vector databases and embedding models, LlamaIndex simplifies the implementation of Retrieval-Augmented Generation (RAG), allowing developers to create context-aware AI assistants with minimal boilerplate code.

Hybrid Search

Sat, 18 Jul 2026 10:01:39 +0000

Definition

Hybrid Search integrates two distinct retrieval methods: dense vector search, which captures semantic meaning and context, and sparse vector (keyword) search, which matches exact terms. By leveraging the strengths of both approaches, it mitigates the limitations of relying on a single method, such as missing synonyms in keyword search or lacking precision in pure semantic search. This approach is widely used in modern enterprise search engines and RAG applications to deliver highly relevant results across diverse query types.

Chunking

Sat, 18 Jul 2026 09:49:17 +0000

Definition

Chunking is a critical preprocessing step in Retrieval-Augmented Generation (RAG) and other NLP pipelines. It involves dividing text into fixed-size or semantic units (chunks) to fit within the context window limits of language models. Effective chunking strategies balance context preservation with retrieval accuracy, ensuring that each segment contains sufficient information to be useful when queried. This technique enables the handling of vast amounts of data that exceed the memory constraints of individual model inputs.

Knowledge Base

Sat, 18 Jul 2026 09:41:26 +0000

Definition

A knowledge base serves as a digital library containing curated data, documents, or facts that AI systems can query to provide accurate, context-aware responses. In modern architectures like Retrieval-Augmented Generation (RAG), it bridges the gap between static pre-trained models and dynamic real-world information. By indexing external data sources, it allows language models to ground their outputs in verified facts, reducing hallucinations and enabling specialized domain expertise without requiring full model retraining.