Enterprise RAG Pipeline

Secure Knowledge Retrieval Architecture

The Problem: Generic LLMs hallucinate or provide outdated answers when queried about proprietary enterprise data. Fine-tuning models is too slow and cost-prohibitive for dynamic documentation.
The User: Internal employees needing instant, accurate answers from proprietary knowledge bases.
The Hypothesis: A Retrieval-Augmented Generation (RAG) pipeline will ground the LLM's responses in internal company truth, reducing hallucination rates to near zero.

Core User Flow: The system searches the internal vector database, retrieves the top 3 most relevant context chunks, and forces the LLM to generate an answer only using those chunks.
In Scope for V1: PDF and Markdown ingestion, semantic search retrieval, strict system prompting, and source citation.
Out of Scope for V1: Real-time data syncing. V1 relies on batch ingestion to validate retrieval quality before scaling infrastructure complexity.

Chunking Strategy: Selected Semantic Chunking over Fixed-size. Fixed chunks destroy the semantic meaning of embeddings, degrading search relevance.
Vector Database: Selected Local FAISS for the MVP. This ensured zero data leakage during prototyping by avoiding third-party cloud databases.

Strict Prompt Engineering: The model is constrained to explicitly output "I do not have enough information" if the answer is not within the retrieved context.
Citation Enforcement: Forced appending of file names and page numbers to every generated claim for human-in-the-loop verification.

Primary Metric (Quality): Hallucination Rate (Target: < 2%), measured using the RAGAS framework for Answer Faithfulness.
Secondary Metric (Performance): Time to First Token (TTFT) < 1.5 seconds.