Leveraging Retrieval-Augmented Generation (RAG) for Custom Bots

Why Out-Of-The-Box LLMs Fail

Pre-trained language models possess vast knowledge but lack access to your company's private data, internal guides, or real-time inventories. They hallucinate answers when queried about internal procedures. Retrieval-Augmented Generation (RAG) solves this by searching your document database first, finding relevant passages, and injecting them into the LLM prompt context to guarantee factual accuracy.

Building the RAG Pipeline

A standard RAG architecture involves three key steps:

1. Document Chunking: Breaking down long PDF manuals into small, semantically cohesive chunks.
2. Vector Embeddings: Converting these text chunks into mathematical vectors and storing them in vector databases like Pinecone or pgvector.
3. Semantic Search: When a user asks a question, the system searches the vector database for matching entries and forwards the text as context to the LLM.