AI Customer Support with RAG
Answer customer questions automatically using your product knowledge base
The Problem
TechHeaven’s support queue is full of the same questions
TechHeaven’s support team receives hundreds of questions every week. The answers exist - in the return policy, the product descriptions, the FAQ, and the order database. But finding and writing each answer still takes human time.
“Where is my order #12847?”
“Can I return opened headphones?”
“Does the Sony WH-1000XM5 work with MacBook Air?”
“Is my laptop still under warranty if the screen cracked?”
“How long does express shipping take to Texas?”
The answer to each question already exists in TechHeaven’s data. The problem is connecting the question to the right piece of information - automatically, accurately, and without hallucinating details that are not in the source.
The Limitation
Why keyword search is not enough
Traditional search returns documents. It cannot synthesize an answer from those documents or reason about what the customer actually needs.
The Solution
Retrieval Augmented Generation
RAG solves this by separating two problems: finding the right information (retrieval) and generating a coherent answer from it (generation). The model does not memorize your data - it looks it up at query time.
This means the AI can only answer from what it retrieves. If the answer is not in the knowledge base, it says so instead of inventing one.
RAG Architecture - TechHeaven Customer Support
Core Concepts
How each piece works
Knowledge Base
The collection of documents the AI can retrieve from. For TechHeaven, this is the return policy, shipping policy, warranty terms, product descriptions, FAQ entries, and structured order data.
Chunking
Documents are too long to fit in a single prompt. Chunking splits them into smaller, overlapping pieces - typically 200-500 tokens each - so the most relevant section can be retrieved without pulling in the entire document.
Embeddings
A numerical representation of a chunk's meaning. Text with similar meaning has similar embeddings, even if the words are different. "Return" and "send back" map to nearby points in embedding space.
Vector Search
The customer's question is embedded using the same model as the knowledge base. The vector database returns the k chunks whose embeddings are closest to the question embedding - the most semantically relevant passages.
Prompt Construction
The retrieved chunks are injected into a prompt template alongside the customer's question. The template instructs the LLM to answer only from the provided context.
Hallucination Prevention
Without grounding, LLMs confidently invent policies that do not exist. RAG prevents this by restricting the LLM to retrieved context. When the knowledge base does not contain the answer, the system responds with a fallback instead of guessing.
Evaluation
RAG systems need ongoing measurement. Key metrics: Retrieval precision (did we retrieve the right chunks?), Answer faithfulness (did the LLM stay within the context?), Answer completeness (did it answer the full question?). Frameworks like RAGAS automate this.
Production
Production architecture
A production RAG system for TechHeaven has two pipelines: an offline ingestion pipeline that keeps the knowledge base current, and an online retrieval pipeline that answers questions in real time.
- 1Pull updated content from Bagisto API
- 2Parse and clean documents
- 3Chunk into 300-token segments with 50-token overlap
- 4Embed each chunk using text-embedding model
- 5Upsert into vector database
- 6Schedule nightly re-index for policy changes
- 1Receive customer question
- 2Embed the question
- 3Retrieve top-5 chunks from vector DB
- 4Inject chunks into prompt template
- 5Call LLM with context-grounded prompt
- 6Return answer + source references
- 7Log question + answer for evaluation
Interactive
Explore the system
Once TechHeaven data is available, these sections will be interactive. You will be able to browse the knowledge base, see how documents are chunked, test questions, and inspect the retrieved context.
Business Impact
What this achieves for TechHeaven
Business Applications
Run the notebook
The Jupyter notebook walks through building the full RAG pipeline on TechHeaven data. No local setup required - runs entirely on Google Colab.
Open in ColabEcommerce AI Series
Ecommerce AI Series
Resources
This playbook
Tech stack
Ecommerce AI Roadmap
Completed
Products, orders, customers, policies, and FAQ
Upcoming