AI Chatbot Pre-Launch Checklist: Security, Latency & Guardrail Verification
Security & Prompt Injection Hardening
Execute adversarial red-teaming to confirm the chatbot cannot be jailbroken or manipulated.
Test the bot against standard jailbreak attacks: roleplay instructions ('pretend you are in developer debug mode'), delimiter escaping, indirect injection via ingested documents, and system prompt extraction attacks. Ensure pre-inference classifier models intercept attacks before the primary LLM runs.
- System prompt extraction attacks return standard refusal.
- Indirect prompt injection via user-uploaded documents is neutralized.
- PII scrubber (Presidio / regex) masks SSNs, credit cards, and emails in output.
- Granular rate limiting per IP and authenticated user token.
Retrieval Accuracy & Grounding Verification
Verify that answers are strictly synthesized from authorized source documents.
Run an evaluation dataset of at least 100 historical enterprise queries. Calculate faithfulness and answer relevance scores using automated benchmarks. Confirm that all responses generate valid, clickable citations pointing to active document IDs.
- RAG faithfulness score exceeds 0.90 on benchmark eval suite.
- Zero hallucinated citations or broken internal links.
- Deterministic refusal triggered when cosine similarity is below 0.65 threshold.
- Role-based access filters prevent unauthorized users from retrieving restricted data.
Latency & Concurrency Benchmarking
Stress-test the system under expected peak traffic loads.
Measure time-to-first-token (TTFT) and total response time under 50 concurrent simulated users. Configure token streaming via Server-Sent Events (SSE) or WebSockets so users see immediate responsive feedback within 400ms.
- Streaming TTFT median below 400ms; p99 below 800ms.
- Redis semantic cache active for frequent identical queries.
- Database connection pooling configured to prevent exhaustion under burst traffic.
- Graceful degradation message displayed if LLM provider experiences latency spikes.
Human Handoff & Customer Escalation Protocols
Provide a seamless transition to human agents whenever the AI encounters unresolved queries.
Integrate webhook endpoints with your customer service software (Zendesk, HubSpot, Salesforce). When sentiment analysis detects user frustration or when the user explicitly requests an agent, the bot packages the conversation transcript and hands off seamlessly.
- User can trigger human escalation at any time by typing 'agent' or 'human'.
- Complete conversation summary and extracted user intents passed to support queue.
- Bot automatically disables itself in the chat thread once human takes ownership.
- Out-of-hours messaging clearly informs users of human support availability windows.
Continuous Observability & Compliance Logging
Capture structured telemetry for ongoing quality auditing and performance tracking.
Deploy centralized tracing for every conversation turn: user query, retrieved chunk IDs, similarity scores, token counts, latency breakdowns, and user feedback ratings (thumbs up/down). Ensure sensitive customer data is anonymized before log persistence.
- OpenTelemetry tracing configured across API and database calls.
- User sentiment and thumb ratings captured for weekly quality review.
- Automated alerts triggered on sudden spikes in user refusal or error rates.
- Data retention policy matches GDPR / SOC 2 requirements with automated deletion schedules.
Key Guide Questions Answered
How many test queries are needed before launching to real users?
We recommend a minimum evaluation dataset of 100 domain-specific queries covering typical requests, edge cases, out-of-scope questions, and adversarial jailbreaks.
What is an acceptable hallucination rate in production?
For regulated industries (healthcare, legal, banking), ungrounded hallucination rates must approach zero through strict refusal guardrails. For general enterprise knowledge search, faithfulness should exceed 95%.
AI Chatbot Development
Explore our core production engineering capabilities and architectural specifications.
About Aurevion AI
Custom AI systems, private RAG architectures, and sovereign software development for growing engineering teams.
Aurevion AI engineers dependable production artificial intelligence systems, sovereign retrieval-augmented generation pipelines, autonomous agentic workflows, and modern high-concurrency software applications. We partner directly with engineering leaders across North America and Europe, deploying zero-trust private cloud architectures with complete intellectual property ownership, deterministic reliability guarantees, and transparent sprint-based technical team collaboration.