How to Build a Custom RAG Application (Retrieval-Augmented Generation)

The Hallucination Problem #

The hype cycle around Large Language Models (LLMs) like GPT-4o and Claude 3.5 has convinced many enterprise executives that they can simply buy an API key and suddenly have an AI that knows their entire business.

This is a dangerous misconception.

If you ask a raw LLM, "What is the standard discount we offered to our Indian distributors in Q3 of last year?", the LLM will fail. It does not have access to your private sales contracts. Because LLMs are designed to generate text, it might confidently hallucinate an answer based on generic internet data, leading to catastrophic business decisions.

You cannot train a base LLM on your private data; it is prohibitively expensive and poses massive security risks.

The industry-standard solution is Retrieval-Augmented Generation (RAG).

What is a RAG Application? #

A RAG architecture separates the "knowledge" from the "reasoning."

Instead of expecting the AI to memorize your data, RAG treats the AI as a smart librarian. When a user asks a question, the application first Retrieves the relevant documents from your private, secure database. Then, it passes those specific documents to the LLM and asks it to Generate an answer based only on the provided context.

This eliminates hallucinations, ensures 100% data security, and provides direct citations to the source documents.

The Blueprint: Building a RAG Pipeline #

Building a custom RAG application requires sophisticated Product Engineering. Here is the technical pipeline Oktuv builds for our B2B SaaS and Enterprise clients.

Step 1: Ingestion & Chunking #

Your proprietary data exists in various formats (PDFs, Notion docs, Zendesk tickets). The first step is to build data pipelines that ingest these documents and break them down into semantic "chunks" (e.g., individual paragraphs of 500 tokens). If you chunk the data poorly, the retrieval engine will fail to find the right context.

Step 2: The Embedding Model #

Once the data is chunked, we pass the text through an Embedding Model (such as OpenAI's text-embedding-3-large). This model mathematically translates the human text into high-dimensional vectors (arrays of floating-point numbers). This allows the system to understand the meaning of the text, rather than just matching exact keywords.

Step 3: The Vector Database (pgvector) #

We store these vectors in a Vector Database. While pinecone and Milvus are popular, Oktuv defaults to Supabase with pgvector. Why? Because pgvector allows us to store the AI embeddings in the exact same PostgreSQL database as your standard user data. This is critical for Enterprise Security. When we execute a similarity search, we can use standard SQL Row Level Security (RLS) to ensure that User A can only retrieve vectors from documents that User A has permission to read.

Step 4: The Agentic Orchestration (LangChain) #

We use an orchestration framework like LangChain to manage the interaction loop.

1. The user asks a question.
2. LangChain converts the question into a vector.
3. LangChain executes a cosine similarity search against Supabase, retrieving the top 5 most mathematically relevant chunks of text.
4. LangChain constructs a secure prompt: "Answer the user's question using ONLY the following context. If you don't know, say you don't know. Context: [Insert Retrieved Chunks]."
5. The LLM generates the final answer.

Step 5: The Frontend Interface (Next.js) #

Finally, we build a lightning-fast, highly responsive frontend using Next.js to stream the LLM's response back to the user in real-time, complete with clickable citations referencing the original PDFs.

Beyond Basic RAG: Advanced Techniques for 2026 #

Basic RAG is easy to build in a weekend. Enterprise-grade RAG takes months of engineering to get right.

To achieve 95%+ accuracy for our clients, Oktuv implements advanced techniques:

• Hybrid Search: Combining vector similarity search with traditional BM25 keyword search to ensure we catch exact product names or serial numbers that vector embeddings might miss.
• Re-ranking: Using a specialized cross-encoder model to re-score and re-order the retrieved documents before sending them to the LLM, ensuring the absolute most relevant context is prioritized.
• Agentic Routing: Building multiple specialized RAG pipelines (one for HR data, one for Engineering data) and using an autonomous AI Agent to route the user's question to the correct database dynamically.

Engineering Your Moat #

An off-the-shelf ChatGPT subscription is not a competitive advantage. Building a highly secure, custom RAG architecture that allows your employees or your software users to instantly query millions of proprietary documents is a massive technical moat.

At Oktuv, we don't just write API wrappers. We are deep-tech Product Engineers. We build custom, secure, scalable RAG architectures that integrate directly into your existing infrastructure.

Stop hallucinating and start executing. Contact Oktuv today to architect your custom AI application.