Question 1

What is RAG and why is it better than fine-tuning for enterprise knowledge?

Accepted Answer

Retrieval-Augmented Generation (RAG) combines the power of large language models (LLMs) with external knowledge bases. Unlike fine-tuning, which modifies the LLM itself, RAG allows the LLM to access and cite up-to-date, proprietary information without retraining. This makes it more cost-effective, easier to update, and reduces the risk of 'hallucinations' by grounding responses in verifiable data, crucial for enterprise applications.

Question 2

How do you prevent the AI from making up answers (hallucination)?

Accepted Answer

We implement several strategies to combat hallucination in RAG systems. This includes robust retrieval mechanisms to ensure relevant context, reranking of retrieved documents for precision, and integrating guardrails like source citation, confidence scoring, and fact-checking layers. By grounding the LLM's responses in verified retrieved information, we significantly reduce the likelihood of fabricated answers.

Question 3

What types of documents can be ingested into a RAG system?

Accepted Answer

Our RAG systems are designed to ingest a wide variety of document types, including PDFs, Word documents, Excel spreadsheets, PowerPoint presentations, text files, HTML pages, and even structured data from databases and APIs. We handle diverse formats by employing advanced parsing, chunking, and embedding techniques to prepare the data for efficient retrieval.

Question 4

How do you keep the knowledge base up to date as documents change?

Accepted Answer

Maintaining an up-to-date knowledge base is critical. We implement automated data ingestion pipelines that monitor source systems for changes, additions, or deletions. When updates occur, the relevant documents are re-processed, re-chunked, and re-embedded into the vector database, ensuring the RAG system always has access to the latest information.

Question 5

Can RAG systems work with confidential data on-premise?

Accepted Answer

Absolutely. NeoBram specializes in deploying RAG systems in secure, on-premise, or private cloud environments. This ensures that confidential and sensitive data never leaves your controlled infrastructure, complying with strict data governance and regulatory requirements while still leveraging the benefits of advanced AI.

Question 6

What is the difference between RAG and a traditional search engine?

Accepted Answer

While both retrieve information, RAG goes a step further. A traditional search engine returns a list of documents or links. RAG, however, retrieves relevant information and then uses an LLM to synthesize that information into a coherent, natural language answer, often with citations. It's about generating an answer, not just finding documents, making it much more powerful for complex queries and knowledge extraction.

RAG & Knowledge Systems
AI That Knows Your Business. Not Just the Internet.

Core Capabilities

Document RAG Pipelines

Multi-Source Knowledge Bases

Conversational Knowledge Assistants

GraphRAG & Relationship Reasoning

Hybrid Search Systems

RAG Evaluation & Optimisation

Industry Use Cases

Manufacturing

BFSI

Pharma

EPC

How It Works

Data Audit & Ingestion

Retrieval Architecture

LLM Integration & Guardrails

Evaluate & Optimise

Our Tech Stack

Frequently Asked Questions

Ready to build smarter AI with your data?

RAG & Knowledge SystemsAI That Knows Your Business. Not Just the Internet.