Prompt Analytics Blueprint: Analyze User Prompts to Improve System Prompts and Templates

Objective: design a structured analytics blueprint to analyze user prompts and guide improvements to system prompts and templates. Input data: a dataset with fields such as user_id, session_id, timestamp, prompt_text, prompt_template_id, system_prompt_used, response_quality_score (0-1), cost_tokens, cost_currency, use_case_category, product_version, device_type, region. Optional: user_feedback, success_metric. Task: produce a detailed plan including sections:
- Data schema: definitions, data types, validation rules, and data quality checks.
- Key metrics: adoption rate of templates, average cost per use_case, prompt length, correlation between prompt_template_id and response_quality_score, time-to-value, churn risk.
- Analytics methods: descriptive statistics, cohort analysis, funnel analysis, regression/causal inference, attribution, and anomaly detection.
- Data pipeline: ingestion, normalization, deduplication, enrichment, privacy-preserving transformations, and scheduling.
- Example analyses: sample SQL-like queries and data processing pseudocode to compute the defined metrics.
- Prompt template optimization plan: list of templates to test, variations (A/B/n), evaluation metrics, and an end-to-end design for testing.
- A/B testing plan: treatment/control design, sample size calculation, duration, success criteria, and stopping rules.
- Dashboards and visualizations: recommended charts and dashboards to monitor usage, costs, and template performance.
- Privacy and compliance: data minimization, access controls, and compliance notes.
- Implementation plan: phased milestones, owners, and risk management.
- Outputs and deliverables: actionable recommendations, prioritized backlog, and a maintenance checklist.

Deliverable: generate a structured blueprint in JSON with fields: overview, data_schema, metrics, analytics_methods, data_pipeline, ab_test_plan, template_optimization, dashboards, privacy, implementation, milestones. Include concrete examples (data types, sample queries) and a concise set of next steps.