Enterprise RAG Q&A Assistant
Enabled natural language queries over enterprise information through a semantic retrieval and LLM generation pipeline.
Software Engineer · January 2021 – August 2025
Software Engineer
DataGrokr Analytics
Python · LLMs · RAG
01 · Context
Overview
A Retrieval-Augmented Generation system that gives enterprise users natural language access to internal information. Users ask questions in plain language and receive grounded, contextual answers drawn from the knowledge base.
Business Problem
Enterprise teams needed answers from internal documents and structured data but lacked a simple way to query that information. Manual document search was slow and inconsistent. As the knowledge base grew, the gap between available information and accessible information widened.
My contribution
Designed and built the full RAG pipeline: document ingestion, chunking and preprocessing, embedding, vector storage, semantic retrieval, and LLM generation. Responsible for architecture decisions, cloud deployment, and integration with enterprise data sources.
02 · System
Solution Approach
A RAG pipeline where enterprise content is processed, embedded, and stored in a vector index. When a user asks a question, it is embedded and matched semantically against the knowledge base to retrieve the most relevant context, which is passed to an LLM to generate a grounded answer.
Workflow
Enterprise Content
Documents, reports, and structured data ingested from internal sources.
Processing & Indexing
Content chunked, preprocessed, embedded, and stored in a vector index.
User Query
User submits a natural language question.
Semantic Retrieval
Query embedded and matched against the vector index to retrieve the most relevant content chunks.
Context Assembly
Retrieved chunks assembled into a structured prompt context for the LLM.
LLM Response
LLM generates a grounded, context-aware answer from the retrieved material.
03 · Engineering
Technical Decisions
RAG chosen over fine-tuning: the knowledge base changes frequently, and RAG allows dynamic updates without retraining, making it a more maintainable long-term architecture.
Semantic chunking strategy applied to preserve context across document boundaries — chunk boundaries aligned with logical content units rather than fixed character counts.
Prompt templates structured to instruct the LLM to answer only from retrieved context, reducing the risk of generating plausible but unsupported answers.
Engineering Challenges
Retrieval precision for domain-specific terminology — internal documents used specialised vocabulary requiring careful handling to ensure semantically similar queries matched relevant chunks.
Chunk size trade-off — smaller chunks improved retrieval precision but sometimes truncated context; larger chunks preserved context but reduced retrieval specificity.
Preventing the LLM from drawing on training knowledge when retrieved context was incomplete — requiring strict prompt constraints and fallback handling.
04 · Outcome
Verified Results
Natural language access to enterprise information without manual document search
Grounded, context-aware answers drawn from the knowledge base rather than model assumptions
Reduced time-to-insight for teams querying internal data
The most important design decision in a RAG system is not the LLM — it is retrieval quality. Getting the right chunks to the model matters more than prompt complexity.
Get in touch
shalinirai450@gmail.com