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Java + AI Readiness Quiz
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Based on the “For JVM teams moving from POC to production” whitepaper.
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We have a current inventory of data sources with sensitivity labels such as PII or PHI
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Regions and residency constraints are documented and enforced
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Redaction or masking rules are defined for prompts, context and outputs
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Secrets and tokens live in a vault and never appear in logs or prompts
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Access control is role based with least privilege for LLM features
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Retention and deletion policies for prompts, traces, and embeddings exist
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Target data sources are defined with clear ownership and freshness rules
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Chunking strategy is chosen and tested on representative documents
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Embedding model is selected and tested
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Vector database is selected and tested
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Retrieval evaluation set exists with precision targets
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Hybrid search or filters are configured when exactness matters
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Output schema is enforced, such as JSON with strict validation
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Rejection and escalation policies for unsafe or low confidence outputs are defined
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Safety checks include content filters and sensitive term blocklists
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End to end traces capture prompt, retrieved context, model, output, and metadata
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Dashboards monitor latency, throughput, token usage, cost, and quality metrics
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An incident playbook exists for rollbacks and deactivation of LLM-based features
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p95 latency targets are defined per use case and visible to SRE
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Cost ceilings per request and per session are defined with alerts
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Caching and reuse strategies exist for prompts and retrieved context
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Token and context size budgets are documented with truncation rules
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Fallback paths are defined, such as a smaller model, a cached answer, or a human handoff
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Cost attribution and guardrails are in place with per-use-case tags, budgets, alerts, and anomaly detection dashboards
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Service template exists in your chosen runtime, such as Quarkus, Spring Boot, etc
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Build and deploy pipeline is containerized with CI and CD
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Configuration allows model and provider switching without code changes
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Secrets, keys, and connection strings are managed outside of code
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Canary or feature flag strategy is in place for LLM-based features
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On-prem option is validated for sensitive data needs such as Jlama, Ollama, or similar
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You got a score of %%YOURSCORE%% out of %%TOTALSCORE%%
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Focus on foundations. Start with data inventory, compliance basics, and a minimal RAG evaluation set. Ship a thin vertical slice that is observable and safe.

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You got a score of %%YOURSCORE%% out of %%TOTALSCORE%%
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You are close to pilot. Tighten guardrails and dashboards, set firm cost and latency targets, and confirm provider switching by configuration.

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You got a score of %%YOURSCORE%% out of %%TOTALSCORE%%
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Ready for a measured rollout. Run a pilot with explicit success criteria, enable canary releases, and prepare a playbook for scale and operations.

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