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Sliding window chunking has become one of the most widely used retrieval strategies because it helps preserve continuity between chunks without requiring complex semantic analysis.

Chunking is the process of splitting documents into smaller retrievable units before embedding and indexing them. In a RAG pipeline: Documents are split into chunks. Each chunk is converted into embeddings. The embeddings are stored in a vector database. User queries retrieve the most relevant chunks. The retrieved chunks are passed to the LLM as context. This means retrieval quality depends heavily on chunk quality.

With Fintech startups mushrooming everywhere there is a rise in demand for AI and Machine Learning as well. More and more Fintech...