Recommendations Copilot
Copilot for Recommendations, using approved data and bounded actions.
BlueprintRecommendations
Learn the customer, business, technology, AI, controls, and KPIs for recommendations.
Learning model: business goal → customer journey → systems/data → failure modes → AI opportunity → human control → KPIs.
Agent patterns
Recommendations Monitor
Monitor for Recommendations, using approved data and bounded actions.
BlueprintRecommendations Recovery Agent
Recovery Agent for Recommendations, using approved data and bounded actions.
BlueprintRecommendations Analyst Agent
Analyst Agent for Recommendations, using approved data and bounded actions.
BlueprintRecommendations QA & Control Agent
QA & Control Agent for Recommendations, using approved data and bounded actions.
Blueprint36 prompts
Recommendations — /human
/human Explain Recommendations to a fresher using a human story, then a D2C example, workflow, risks, and KPIs.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /visualize
/visualize Visualize Recommendations as an end-to-end D2C flow with customer actions, systems, decisions, exceptions, and outcomes.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /diagram
/diagram Create an architecture diagram for Recommendations, showing storefront, services, APIs, data, events, and human approvals.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /flowchart
/flowchart Create a decision flowchart for Recommendations, including normal path, exception path, retry, escalation, and safe stop.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /mindmap
/mindmap Create a mind map for Recommendations: customer, business, data, technology, AI, risks, KPIs, and owners.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /analysis
/analysis Analyse Recommendations performance for a fictional D2C electronics business. Segment results, find root causes, quantify impact, and recommend actions.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /RCA
/RCA Create an RCA for a failure in Recommendations: timeline, symptoms, hypotheses, evidence, root cause, containment, corrective action, prevention, and owner.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /BRD
/BRD Write a detailed BRD for improving Recommendations: problem, users, scope, process, rules, integrations, errors, analytics, acceptance criteria, and exclusions.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /PRD
/PRD Create a product requirements document for an AI-enabled Recommendations capability with user needs, UX, logic, APIs, controls, metrics, and rollout.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /userstories
/userstories Write user stories and acceptance criteria for Recommendations across customer, operations, support, product, and admin personas.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /testcases
/testcases Generate 50 test cases for Recommendations: happy, negative, boundary, integration, performance, security, accessibility, and business-rule cases.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /UAT
/UAT Create a UAT plan for Recommendations with scenarios, preconditions, data, steps, expected result, owner, severity, and sign-off.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /API
/API Design APIs for Recommendations: endpoints, request/response JSON, validation, idempotency, errors, auth, rate limits, and observability.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /JSON
/JSON Create realistic JSON payload examples for Recommendations, including success, failure, partial, retry, and exception states.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /SQL
/SQL Write SQL analysis questions and example queries for Recommendations: funnel, failures, cohorts, anomalies, SLA, and business impact.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /dashboard
/dashboard Design an executive and operations dashboard for Recommendations with metric formulas, dimensions, thresholds, alerts, owners, and decisions.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /KPI
/KPI Define a KPI tree for Recommendations: north-star, leading, lagging, customer, commercial, operational, quality, and risk metrics.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /agent
/agent Design an AI agent for Recommendations: objective, trigger, state, tools, APIs, memory, rules, confidence, approval, audit log, KPIs, and tests.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /multiagent
/multiagent Design a multi-agent workflow for Recommendations, separating planner, specialist agents, deterministic services, and human approval.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /automation
/automation Map Recommendations and label every step: deterministic automation, AI assist, agent action, human approval, or human only.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /guardrails
/guardrails Create AI guardrails for Recommendations: permissions, PII, policy grounding, confidence, financial thresholds, irreversible actions, audit, and rollback.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /evaluate
/evaluate Create an evaluation framework for AI in Recommendations: accuracy, hallucination, precision/recall, latency, cost, safety, and business impact.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /monitor
/monitor Design production monitoring for AI-enabled Recommendations: technical health, model quality, drift, customer harm, business metrics, and alerts.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /incident
/incident Create an incident playbook for Recommendations: detect, classify, contain, communicate, recover, reconcile, and learn.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /SOP
/SOP Write an operations SOP for Recommendations with roles, prerequisites, normal process, exception handling, escalation matrix, and daily controls.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /checklist
/checklist Create a launch-readiness checklist for Recommendations: business, UX, content, data, API, QA, security, analytics, support, and rollback.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /roadmap
/roadmap Create a 90-day roadmap to improve Recommendations: baseline, quick wins, experiments, AI opportunities, dependencies, milestones, and KPIs.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /prioritize
/prioritize Prioritise 15 improvement ideas for Recommendations using impact, effort, confidence, customer value, risk, and dependency.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /executive
/executive Create a one-page executive review for Recommendations: performance, customer impact, revenue impact, top risks, actions, owners, and decisions.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /interview
/interview Create 20 interview questions and model answers about Recommendations for ecommerce, product, and AI manager roles.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /quiz
/quiz Create a 15-question quiz on Recommendations: beginner to advanced, with answers and explanations.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /project
/project Create a portfolio project for Recommendations using fictional data: problem, dataset, analysis, AI solution, architecture, prompts, evaluation, and presentation.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /promptEvolution
/promptEvolution Improve a weak Recommendations prompt through beginner, structured, expert, and agent-level versions.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /sales
/sales Create five D2C sales scenarios for Recommendations, including intent, recommendation logic, objection, guardrail, and conversion KPI.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /cs
/cs Create five customer-service scenarios for Recommendations, including customer message, system checks, grounded response, escalation, and KPI.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.
Recommendations — /ops
/ops Create five operations scenarios for Recommendations, with exception, evidence, decision, owner, SLA, and preventive control.Expected: Structured answer with assumptions, data, workflow, controls, KPIs, and actions.