How to measure ROI for an AI pilot (without guessing)
Define baseline, target metric, and adoption workflow—before you touch a model.
Template: Baseline → Intervention → Measure → Iterate
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Practical AI deployment notes
No buzzwords—just playbooks for shipping AI safely: evaluation, monitoring, cost controls, integration patterns, and adoption.
What you’ll find here
- AI pilot ROI templates
- LLM/RAG reliability and evaluation
- MLOps deployment and monitoring
- Security and governance basics
Latest posts
RAG that works: relevance tuning beats prompt tweaking
If the retrieved context is wrong, the model will be wrong. Start with retrieval quality.
Checklist: chunking, filtering, evaluation set
Explore LLM integrationProduction LLM cost controls: the non-negotiables
Budgets don’t blow up because of one request—they blow up due to missing guardrails.
Controls: limits, caching, routing, monitoring
Harden productionLLM safety in enterprises: guardrails that actually hold up
Safety isn’t a single prompt. It’s role-based access, retrieval boundaries, and measurable evaluation.
Focus: RBAC, policy constraints, auditability
Discuss requirementsWhy AI projects fail after the demo (and how to prevent it)
The missing ingredient is operational ownership: monitoring, workflows, and feedback loops.
Deliverable: runbooks and release process
See servicesData foundations for AI: what to fix first
A small set of fixes unlocks most pilots: consistency, lineage, and access patterns.
Start with: sources, definitions, and monitoring
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