IntelliQuery

Introduction

Welcome to the brain of our AI-powered knowledge machine — the . If you've ever wished your chatbot could actually know things instead of making wild guesses, you’re in the right place. This system bridges the gap between static AI models and dynamic, real-world information by giving the AI a way to retrieve and reason in real time.

What is a RAG Pipeline?

RAG stands for — which sounds complex, but think of it like this: Instead of forcing an AI to remember every detail (like an overworked student before exams), we let it look things up when needed.

The pipeline first the most relevant information from a knowledge base, and then the AI model a thoughtful response based on that data. The result? Smarter, more accurate answers that actually make sense in context.

Motivation

Modern large language models are powerful, but they have two practical limitations: they don't retain every piece of domain knowledge, and their training cutoff means they can be out of date. A solves both by letting the model look up relevant facts at query time and then generate responses grounded in that evidence. The result is more accurate answers, up-to-date knowledge, and reduced hallucination.

Instead of forcing a single model to "remember" everything, we give it a fast way to retrieve the best snippets from a and synthesize a concise, source-backed response. This improves reliability and makes the system practical for real-world production use.

Industry use cases

Customer support: fetch product docs and KB articles to answer tickets with citations.
Legal & compliance: surface contract clauses, precedents, and regulatory text with provenance.
Healthcare: present up-to-date clinical guidelines and patient-specific data (with proper controls).
Enterprise search & knowledge management: make internal docs, wikis, and SOPs queryable with high relevance.
R&D and scientific discovery: retrieve papers and notes to accelerate literature review.
Developer tools: produce accurate code examples by pulling from internal repos, API docs, and changelogs.
E-commerce: combine product catalogs and user history to generate contextual recommendations.

How it increases efficiency

Smaller, cheaper model calls — only the most relevant context is sent to the model.
Faster iteration — the knowledge base can be updated independently of model training.
Improved throughput — efficient retrieval + caching reduces repeated expensive generation.
Focused context windows — chunking and selective retrieval keep prompts small and precise.

Reducing hallucinations

The core anti-hallucination strategy is grounding. By returning relevant document snippets (and optionally their source metadata) alongside generated text, the system forces the model to reason from verifiable facts. Additional safeguards include retrieval re-ranking, answer verification, source scoring, and conservative response policies when confidence is low.

What's in my Vector Store?

Following documents are stored in my Vector Database:

Documentation