4 posts tagged with "rag"

Beyond Keyword Matching​

Imagine searching for a document using the phrase: "How can I improve application performance?"

You know the information exists, but the search system returns little or nothing useful because the document contains terms such as "latency optimization," "resource utilization," or "performance tuning" instead of the exact words used in the query.

The information is relevant. The wording is simply different.

This is one of the biggest limitations of traditional search systems, especially when working with large volumes of technical or domain-specific data.

Large Language Models (LLMs) are increasingly being used for structured information extraction from documents such as resumes, invoices, and reports. However, different LLMs behave differently in terms of extraction accuracy, execution time, consistency, and output quality. Choosing the right model for document extraction tasks therefore becomes an important challenge.

To address this, we built an LLM Document Extraction Benchmark System that compares multiple LLMs on structured document extraction tasks. The framework evaluates models using common prompts and documents, then measures their performance using metrics such as execution time, accuracy, precision, recall, and F1 score.

Imagine a company handling thousands of resumes. Now imagine trying to search for the right information using only exact keywords. Traditional search systems often fail because they mainly rely on keyword matching instead of understanding meaning.

This is where vector databases shine with semantic search.

A chatbot is a software application that uses AI to have conversations with users, helping them find information or answer questions. We built this chatbot using Retrieval-Augmented Generation (RAG) to improve its responses, Neo4j to store structured data, and Large Language Models (LLMs) to understand and generate natural language.

We created 2 types of Nodes/Labels, "Bank" and "Owner" and 1 type of relationship between them: "IS_OWNED_BY". The blog below lays out how we created a chatbot to query the relationship between the Node Types mentioned using RAG (Retrieval Augmented Generation) techniques.