AI Therapy Chatbot Developer Interview Questions

A strong answer covers determinism and safety of rule-based systems vs. flexibility and naturalness of LLMs, plus hallucination risk as the key trade-off.

These are validated clinical screening tools for depression and anxiety; the answer should explain how they enable outcome measurement and evidence-based design.

Answer should reference administrative, physical, and technical safeguards and explain why protected health information (PHI) in chat logs requires special handling.

Therapeutic alliance is the trust and rapport between therapist and client; a good answer explores whether and how an AI can establish rapport and why user trust affects outcomes.

The answer should define prompt engineering and show a concrete system prompt that instructs the LLM to use Socratic questioning, cognitive restructuring, and never provide diagnoses.

A strong answer walks through the five columns of a CBT thought record (situation, thought, emotion, evidence, alternative thought) as dialogue states with branching logic.

Answer should cover how RAG grounds responses in curated clinical knowledge, reduces hallucination, allows easy content updates, and complements fine-tuning for tone and style.

Cover parameter efficiency, compute costs, catastrophic forgetting risks, and how LoRA is ideal for domain adaptation while full fine-tuning suits large-scale behavioral changes.

A good answer discusses onboarding flows, initial PHQ-9/GAD-7 assessments, rapport-building small talk, informed consent, and calibrating response tone to early user signals.

Cover managed vs. self-hosted trade-offs, filtering capabilities for clinical metadata, cost at scale, HIPAA compliance considerations, and hybrid search support.

Strong answers include safety incident rate, hallucination rate, empathy scoring (LLM-as-judge), user retention, PHQ-9 score trajectory, escalation accuracy, and response latency.

Cover session summarization stored in encrypted databases, consent-based recall, data retention policies, and the balance between personalization and data minimization.

Should cover output filtering, topic restriction (no diagnoses, no medication advice), crisis keyword detection, and structured output schemas using tools like Guardrails AI or NeMo.

Cover clinician involvement in prompt template review, response auditing, red-team participation, outcome metric design, and regular clinical fidelity reviews.

Discuss randomization strategy, primary metrics (PHQ-9 change, engagement), secondary metrics (safety incidents), sample size calculation, ethical considerations of holding back care, and IRB considerations.

A strong answer covers real-time signal classification, async message queue for low-latency handoff, warm handoff protocols with context transfer, fallback to 988 hotlines, and post-escalation logging.

Cover categories: jailbreak prompts, boundary testing, crisis simulation, adversarial persona adoption, prompt injection via user input, and feedback loops into guardrail updates.

Discuss RAG grounding, citation of sources, confidence scoring, abstention policies ('I'm not sure, let me connect you with a human'), and clinical fact-checking pipelines.

Cover multimodal sentiment analysis, emotion classifiers on text and optional voice prosody, dynamic prompt template switching, and ethical limits of emotion inference.

Discuss the spectrum from wellness (low regulatory burden) to SaMD (FDA clearance required), the role of intended use claims, and how marketing language affects classification.

Cover differential privacy, secure aggregation, institutional data silos, model update protocols, and the tension between model improvement and strict data governance.

Discuss RCT design, control groups (waitlist, human therapy, app-only), validated outcome measures, follow-up intervals, confounding variables, and publication strategy.

Cover culturally adapted therapeutic frameworks, multilingual fine-tuning, locale-specific crisis resources, avoiding Western-centric therapeutic assumptions, and community advisory boards.

Discuss the ethics of paternalism vs. autonomy in AI therapy, graduated response models, transparent disclosure of limitations, and respecting user agency while maintaining safety floors.

Cover autoscaling with Kubernetes, streaming LLM responses, encrypted session state in Redis, regional data residency requirements, and load testing with synthetic therapeutic conversations.

Strong answer covers immediate crisis classification, empathetic acknowledgment response, warm handoff trigger to 988 or crisis counselor, session logging, and post-incident clinical review.

Cover response analysis across temperature and prompt settings, empathy scoring rubrics, prompt template revision with therapist input, A/B testing new prompts, and regression testing to prevent other quality drops.

Discuss clear boundary-setting responses, topic classification to detect medical advice requests, escalation to prescribing provider, and proactive design to avoid enabling medication substitution.

Cover age verification flows, consent management systems, jurisdiction-aware logic, data handling for minors (COPPA), and escalation paths to appropriate youth services.

Cover immediate conversation log review, root cause analysis, public communication strategy, clinician-led safety audit, guardrail updates, external expert review, and regulatory notification if required.

Discuss language-specific evaluation datasets, multilingual clinician reviewers, non-English RAG content, performance gap measurement, and a phased rollout of properly validated language support.

Cover FHIR API integration, clinical summary generation (not raw transcripts), therapist review UI, data mapping between chatbot metadata and EHR fields, and consent management.

Discuss session-level behavioral analytics, persona drift detection, graceful refocusing responses, session termination policies, and adversarial input pattern logging.

Discuss minimum safety standards as non-negotiable contract terms, safety certification requirements for partners, audit rights, and the ethical obligation to maintain safety floors regardless of commercial pressure.

Cover severity-stratified analysis, the implication that moderate-severe cases may need human escalation, adaptive treatment protocols by severity, and communicating appropriate scope limitations to users.

Cover document chunking strategies (section-aware vs. fixed-size), embedding model selection, vector store indexing, retrieval with reranking, context window assembly, and prompt template with retrieved context injection.

Discuss conversational memory types (buffer, summary), tool nodes (crisis detector, PHQ-9 scorer, content retriever), conditional routing, and how guardrails integrate as middleware layers.

Cover test dataset curation (safe, borderline, crisis scenarios), metric definitions (hallucination, bias, empathy), CI/CD integration, threshold-based alerts, and human review of flagged failures.

Cover dataset preparation (conversation formatting, quality filtering), LoRA config (rank, target modules), training hyperparameters, evaluation during training, and post-training safety validation before deployment.

Describe the cascade: regex/keyword first pass, sentiment analysis second pass, fine-tuned classifier third pass, LLM-as-judge for ambiguous cases, and how each layer has different precision/recall trade-offs.

Cover experiment naming conventions, custom metrics logging (safety scores, empathy ratings, latency), sweep configurations for prompt optimization, and model registry for approved production models.

Discuss sampling strategies (random, high-uncertainty, new topics), review UI with inline editing, feedback-to-training-data pipeline, weekly clinician review sprints, and versioning of clinician-approved response templates.

Cover GitLab/GitHub CI with security scanning, Docker image builds, SageMaker or ECS deployment, encrypted environment variables, blue-green deployment, canary traffic shifting, and automated rollback on safety metric regression.

Cover rail configuration (topical rails, output rails), custom colang flows for medical topic detection, fallback responses, and testing the guardrails with adversarial probe datasets.

Cover PII removal (names, locations, dates), clinical entity preservation (symptoms, emotions), de-identification validation, IRB approval processes, data augmentation for underrepresented scenarios, and secure data handling chain.

Strong answers show principled advocacy, data-backed reasoning, creative compromise solutions, and the ability to escalate when necessary while maintaining working relationships.

Cover specific habits: reading arXiv papers, attending conferences (NeurIPS health workshops, APA tech symposiums), clinical advisory boards, hands-on experimentation, and peer communities.

Look for intellectual humility, genuine curiosity about clinical perspective, concrete actions taken to incorporate feedback, and how the collaboration improved the product.

Authentic answers discuss boundaries, professional support, the distinction between empathy and emotional enmeshment, and how personal wellbeing practices sustain long-term work in this domain.

Strong answers demonstrate ethical courage, specific escalation steps, documentation of concerns, willingness to leave if necessary, and understanding of regulatory reporting obligations.