In RAG (Retrieval-Augmented Generation), chunking means splitting large documents into smaller pieces so they can be embedded, stored in a vector database, and retrieved efficiently.

In interviews, you’re expected to explain why chunking matters and what strategies exist, not just list them.

In RAG (Retrieval-Augmented Generation) systems, rerankers are a critical component used to improve the quality of retrieved documents before they are passed to the LLM for generation.

BM25 and Vector Search are two different approaches used in information retrieval systems, especially in search engines and RAG (Retrieval-Augmented Generation) systems.

In Retrieval-Augmented Generation (RAG), chunking means splitting large documents into smaller pieces called chunks before storing them in a vector database.

These chunks are later retrieved and sent to the LLM as context for answering questions.

In Retrieval-Augmented Generation systems, vector databases are used to store and retrieve embeddings efficiently so the LLM can access relevant external knowledge during response generation.

1. User asks question

2. System converts question into embeddings

3. Vector DB searches relevant documents

4. Retrieved context is added to prompt

5. LLM generates answer using retrieved data

In Retrieval-Augmented Generation, the retrieval step is responsible for finding relevant external information before the LLM generates an answer.

Without retrieval, the LLM only relies on its pre-trained knowledge.
With retrieval, it can fetch updated or domain-specific data from documents, databases, PDFs, websites, or vector stores.

RAG (Retrieval-Augmented Generation) is used because LLMs have limitations even though they are trained on huge amounts of data.

RAG (Retrieval-Augmented Generation) is an AI architecture that combines:

1. Retrieval system → searches relevant information from external sources

2. Generation model (LLM) → generates a response using the retrieved information

Instead of relying only on the model’s training data, RAG allows the AI to fetch up-to-date or domain-specific knowledge before answering.

Optimizing a React application means improving its performance, speed, memory usage, and rendering efficiency so the UI feels fast and responsive.