Response quality

Bookbag is built around one idea: an answer you can trust is an answer grounded in your data. This page explains how that grounding works and the levers you control.

Retrieval-augmented generation

When a customer asks something, Bookbag doesn't send the question straight to a language model. It first retrieves the most relevant chunks from your indexed sources, then asks the model to answer using only those chunks. The model composes a natural reply, but the facts come from your data.

1. 1
   Embed

   Every source is split into chunks and embedded into a vector index when you train it.
2. 2
   Retrieve

   The customer's question is embedded and matched against the index to find the most relevant chunks.
3. 3
   Ground

   Those chunks are inserted into the prompt as the only allowed source of truth.
4. 4
   Cite

   The reply includes citations back to the sources used, so answers are auditable.

Citations

Every grounded answer carries citations to the chunks it used. In the playground you can expand them to verify the agent is pulling from the right place — and on the widget, customers can see where an answer came from. Citations are the fastest way to debug a wrong answer: if the cited chunk is wrong or missing, fix the source.

Q&A priority

Q&A pairs are treated as authoritative. When a question closely matches a Q&A pair, that exact answer is returned, short-circuiting paraphrase. Use Q&A for the answers that must be exact.

Fallbacks and "I don't know"

When retrieval finds nothing relevant, a well-configured agent says so and offers to connect the customer with a human, rather than guessing. You set the fallback message in the agent's settings, and you can pair it with the Escalate to a human action so the conversation lands in your help desk.

Measuring quality

Two tools tell you how you're doing:

• Analytics — resolution rate, conversation volume, and escalation trends over time.
• Support Audit — paste real transcripts and get a scored report on accuracy, hallucination rate, and resolution, judged by an LLM.

What's next