RAG (Retrieval-Augmented Generation)

RAG is how modern AI chatbots answer questions about your specific business without hallucinating. Without RAG, an LLM responds from its training data — which doesn’t include your product catalogue, your return policy, or your internal KB articles. With RAG, the LLM first searches your documents, retrieves the relevant chunks, and uses them as ground truth in its response.

Why it matters for chatbot platform selection

Most AI chatbot platforms in 2026 use RAG under the hood, but they differ dramatically in:

1. What data sources they can retrieve from — Tidio retrieves from Shopify product data natively; Chatbase retrieves from uploaded PDFs; Botpress retrieves from custom vector stores you configure
2. How frequently the retrieval index refreshes — Tidio syncs Shopify data in near-real-time; Chatbase requires manual re-upload; Zendesk AI syncs from Zendesk Guide automatically
3. Whether you can see which document chunk was retrieved — most no-code platforms hide this; developer frameworks like Botpress and Voiceflow expose it

The hallucination risk: RAG reduces but does not eliminate hallucination. If the retrieved chunk is outdated or ambiguous, the LLM can still produce a plausible-sounding wrong answer. In our test batch of 50 transcripts, RAG-grounded responses hallucinated at 4–8% vs 25–40% for ungrounded responses.

How RAG works in practice

1. Index creation — your documents (help articles, product data, policies) are split into chunks and converted to vector embeddings by an embedding model (OpenAI, Cohere, or a local model)
2. Storage — the vectors are stored in a vector database (Pinecone, Weaviate, pgvector, Qdrant — or the platform’s proprietary store)
3. Retrieval — when a user sends a message, the platform converts it to a query vector and finds the N most similar document chunks
4. Generation — the LLM receives the user’s message + the retrieved chunks + the system prompt and generates a response grounded in your actual docs

Practical example

A Tidio user on a Shopify store asks: “Is the Acme Widget in stock in size M?”

1. Tidio’s retrieval system searches the Shopify product catalogue for “Acme Widget size M”
2. Retrieves the product variant record: {sku: "AW-M", inventory_quantity: 12, price: 29.99}
3. Passes this to Lyro’s LLM with the user’s message
4. Lyro responds: “Yes — we have 12 Acme Widgets in size M in stock at $29.99. Want me to add one to your cart?”

Without RAG, Lyro would either refuse to answer or hallucinate inventory data. With RAG and live Shopify sync, it retrieves the authoritative answer.

When RAG isn’t enough

RAG fails when:

• The knowledge base is incomplete or outdated — the AI can only retrieve what’s in the index; gaps produce “I don’t know” or hallucinated answers
• The user’s query requires multi-step reasoning — RAG retrieves relevant chunks but doesn’t reason across them; for complex queries, function calling (the AI calling your API directly) produces better results
• The documents contradict each other — multiple retrieved chunks with conflicting information confuse the LLM