Semantic Search with LLM – RAG-Powered Retrieval System
Overview
This project is an intelligent search system that understands the meaning and context of a query instead of relying only on exact keyword matches. It finds relevant information even when users phrase questions differently from the source content.
Documents are preprocessed and converted into embeddings, stored as vector representations and searched using semantic similarity. The most relevant passages are retrieved and passed to an LLM, following Retrieval-Augmented Generation (RAG) principles, to produce accurate, grounded answers.
The system suits knowledge bases, internal documentation, support portals and research tools where users need precise answers fast. The team designed the semantic-search workflow, worked with embeddings and vector retrieval, integrated LLM responses and evaluated retrieval relevance to improve answer quality.
Key Features
Meaning-Based Search
Understands the intent and context behind queries, returning relevant results beyond exact keyword matches.
Embeddings & Vector Representations
Converts documents into high-dimensional embeddings that capture semantic meaning for accurate matching.
Fast Vector Search
Uses similarity search over a vector index to find the most relevant passages in milliseconds.
Retrieval-Augmented Generation
Feeds retrieved context to an LLM so answers are grounded in your own data, reducing hallucinations.
Relevance Evaluation
Retrieval quality is measured and tuned to keep the most useful context at the top of every result set.
Contextual Answer Generation
Produces clear, natural-language answers that summarise and connect information from multiple sources.
Project Details
Developed By
Pristient Orbit
Duration
1 Month
Tech Stack
Pristient Orbit transformed our vision into reality. Their technical expertise and dedication to quality resulted in a product that exceeded our expectations and significantly accelerated our growth.
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