A rigorous checklist covering persistent memory deletion, training opt-outs, human review policies, and export portability.
1. Core Architectural Principles & Parameter Tuning
Designing a high-fidelity AI companion experience requires balancing temperature settings, memory vector indexing, and robust privacy guardrails. Proper configuration ensures sustained conversational immersion without erratic model drift.
- System Prompt Precision: Establishing consistent conversational personas, linguistic boundaries, and tone registers.
- Episodic Vector Retention: Utilizing RAG frameworks to preserve multi-session memories and historical context.
- Privacy & Consent Architecture: Verifying client-side data wiping, opt-outs for training data, and end-to-end encryption.
Audit Privacy Controls on Candy AI
Inspect account security, TLS 1.3 encryption, and self-serve history wiping.
2. Implementation Best Practices
By applying structured prompting methodologies and monitoring context recall, users achieve dependable, high-empathy companionship tailored to their exact conversational expectations.
