What is RAG?

What Is RAG?

RAG, which stands for Retrieval-Augmented Generation, is a technique that combines the creative power of generative AI with the accuracy of information retrieval. Instead of relying solely on what an AI model learned during training, RAG retrieves relevant information from external sources (your company documents, databases, knowledge bases) and provides that information to the model as context when generating a response.

The concept is intuitive: rather than asking someone to answer a question purely from memory, you give them access to the relevant documents first. The result is answers that are more accurate, more current, and grounded in your specific business data.

How RAG Works

The RAG process involves three main steps:

1. Indexing (Setup Phase)

Your business documents, knowledge base articles, product information, policies, and other relevant data are processed and stored in a vector database. Each piece of content is converted into a mathematical representation (called an embedding) that captures its meaning. This indexing happens once and is updated as your data changes.

2. Retrieval (At Query Time)

When a user asks a question, the system converts the question into the same type of mathematical representation and searches the vector database for the most semantically similar content. For example, if someone asks "What is our return policy for electronics?", the system retrieves the specific sections of your return policy that relate to electronics.

3. Generation (Response Creation)

The retrieved information is combined with the user's question and sent to the AI model as context. The model then generates a response that draws on both its general knowledge and the specific retrieved information. The result is an answer that is informed by your actual business data.

Why RAG Matters for Business

RAG solves several critical problems that limit the usefulness of AI in business settings:

Accuracy and Grounding Without RAG, an AI model can only draw on what it learned during training, which may be outdated, generic, or simply wrong for your specific context. RAG grounds the model's responses in your actual data, dramatically reducing the risk of the model generating plausible but incorrect information (hallucination).

Currency of Information AI models have a knowledge cutoff date and do not know about events or changes that occurred after their training. RAG allows the model to access your most current information -- latest product updates, recent policy changes, current pricing -- without needing to retrain the model.

Domain Specificity Your business has unique products, processes, terminology, and policies that no general-purpose AI model can know about. RAG lets you make all of this institutional knowledge available to the AI, turning it into a specialist in your business.

Data Privacy RAG keeps your data in your own systems. The AI model receives only the relevant snippets needed to answer each specific question, and you control what data is accessible and to whom.

RAG vs. Fine-tuning

Business leaders often ask whether they should use RAG or fine-tuning. The answer depends on the use case:

Factor	RAG	Fine-tuning
Best for	Factual Q&A, current information	Style, format, behavioral changes
Data freshness	Always current	Frozen at training time
Setup complexity	Moderate (vector DB, retrieval pipeline)	Lower (upload data, train)
Ongoing maintenance	Update documents as they change	Retrain periodically
Transparency	Can show source documents	Less transparent
Cost	Infrastructure + per-query retrieval	One-time training + inference

Many businesses use both techniques together: fine-tuning to adjust the model's style and behavior, and RAG to ensure it has access to accurate, current information.

Implementing RAG in Practice

Technology Stack

A typical RAG implementation includes:

• Vector database: Pinecone, Weaviate, Qdrant, or pgvector (PostgreSQL extension)
• Embedding model: OpenAI embeddings, Cohere embed, or open-source alternatives
• LLM: GPT-4, Claude, Gemini, or an open-source model
• Orchestration framework: LangChain, LlamaIndex, or custom implementation

Common Use Cases in Southeast Asia

Internal Knowledge Management Companies deploy RAG-powered systems that let employees ask questions about company policies, processes, and procedures in natural language. This is particularly valuable for organizations with large workforces across multiple ASEAN countries who need consistent access to the same information.

Customer Support RAG enables AI chatbots that can answer detailed questions about your specific products, services, and policies. A bank in Thailand can deploy a support bot that accurately answers questions about its specific account types, fees, and regulatory requirements rather than giving generic banking advice.

Document Intelligence Legal firms, accounting practices, and consulting companies use RAG to build systems that can answer questions across large document collections. A lawyer can ask a question and receive an answer backed by specific clauses from relevant contracts or regulations.

Sales Enablement Sales teams use RAG-powered tools to quickly find relevant case studies, product specifications, competitive comparisons, and pricing information during customer conversations.

Best Practices for RAG Implementation

• Chunk your documents thoughtfully: How you split documents into smaller pieces significantly impacts retrieval quality. Experiment with different chunk sizes and overlap strategies.
• Invest in data quality: RAG is only as good as the data it retrieves. Ensure your knowledge base is accurate, well-organized, and regularly updated.
• Include metadata: Tag your documents with metadata like date, department, product line, and document type to enable filtered retrieval.
• Test retrieval quality separately: Before evaluating the full RAG system, test whether the retrieval component is finding the right documents. Poor retrieval cannot be compensated for by a better LLM.
• Implement citation: Configure your system to reference the source documents for each response, enabling users to verify accuracy.