10.1 Customer Support GenAI Assistant Lab

Lab scenario: support assistant for a SaaS help desk

A midmarket SaaS company wants to reduce agent handle time and improve answer consistency. Support agents currently search a product documentation site, internal troubleshooting articles, release notes, and a ticketing system. Leadership asks for a generative AI assistant that drafts answers, cites approved sources, summarizes customer conversations, and suggests when a case should be escalated. The business does not want the assistant to make billing adjustments or close cases automatically during the pilot.

The first practitioner move is to define the job clearly. This is not a general chatbot project. The pilot should answer questions from approved support content, draft agent-facing replies, summarize case history, and identify missing facts. Customer-facing automation may come later only after quality, safety, and escalation controls are proven. A non-builder should ask what the assistant is allowed to do, what it must refuse, and who remains accountable for the final customer response.

Build the pilot as an agent-assist experience first. A support agent opens a case, selects Ask Assistant, and the app sends the case subject, product, customer tier, and a redacted issue description to Bedrock. The Knowledge Base retrieves approved passages from product docs and troubleshooting articles. The model drafts a response with citations and a confidence note. The agent edits the draft, sends it through the normal support tool, and tags whether the answer was useful. This design keeps a human in the loop while the team learns where the assistant helps.

The decision log should be written before implementation starts. Record the selected foundation model, backup model, source repositories, chunking and metadata plan, PII handling rule, guardrail policies, logging destinations, retention settings, and pilot metrics. Include business metrics such as average handle time, first-contact resolution, escalation rate, agent satisfaction, and customer complaint rate. Also include a stop condition, such as repeated unsupported answers in a regulated topic or evidence that agents are copying drafts without review.

End-to-end lab workflow:

1. Choose two support categories with high volume and low regulatory risk, such as login issues and product setup questions.
2. Curate approved articles in S3 or a supported content source, remove stale documents, and assign a content owner.
3. Create metadata for product, version, audience, region, and effective date so retrieval can filter the right sources.
4. Configure a Bedrock Knowledge Base and test retrieval separately from generation.
5. Compare two candidate models with the same prompt template, source set, and scoring rubric.
6. Add Guardrails for prompt attacks, unsafe content, denied topics, and sensitive information handling.
7. Integrate the draft into the support UI with citations, refusal behavior, feedback capture, and escalation options.
8. Monitor usage, latency, cost, blocked prompts, retrieval misses, and agent edits during the pilot.

Common failure modes should be reviewed as part of the lab, not discovered after launch. The assistant may cite the wrong product version because metadata is missing. It may answer from an old article because the content owner did not retire stale pages. It may hallucinate when retrieval returns weak passages. It may leak customer details if prompts, logs, or feedback fields are not redacted. It may follow a prompt injection hidden in a customer email. It may suggest a refund or credit even though the pilot explicitly excludes financial actions.

The fallback plan is as important as the happy path. If retrieval fails, the assistant should say that the approved sources do not contain enough information and offer search results or escalation. If a prompt is blocked, the UI should show a clear internal message and route the case normally. If Bedrock invocation is throttled or unavailable, agents should continue with existing tools. If citations disagree, the assistant should surface the conflict rather than inventing a tie-breaker.

Review prompts before the quiz:

• Which support categories are safe enough for a first pilot, and which should stay human-only?
• Which fields must be redacted before prompts or logs are stored?
• How will the team know whether a bad answer came from retrieval, prompt design, model choice, or stale source content?
• What action is the assistant explicitly not allowed to perform?
• Who owns article freshness, guardrail tuning, cost review, and support agent training?