Start with the technical decision in front of you, then inspect the evidence, limitations and implementation guidance behind it.
Each desk connects evidence to a build, buy, deploy, evaluate or stop decision.
Search across titles, summaries, evidence status and tags, or narrow the library to one decision desk.
A release decision for retiring model IDs and replacement versions
A replacement model is a new configured system, not a dependency patch. Inventory every route, freeze the decision contract, shadow the replacement, and migrate only the scopes that clear explicit quality, safety, cost and rollback gates.
Approve, constrain, test or reject one configured data path
“No training” is not a retention policy, and a region selector is not a complete data map. Use this evidence-led review to gate an AI API on storage, processing, logs, application state, transfers and deletion.
Build, buy or extend without trapping the evidence in one dashboard
Choose an LLM evaluation platform by what you can export, reproduce and migrate—not by grader count. Own the cases, decision rules, provenance and case-level results before adopting a workflow or dashboard.
An implementation decision for agent and automation controls
Decide which AI actions need human approval, what evidence a reviewer must see, and when approval is too weak to make an unsafe authority path deployable.
A build, buy or hybrid decision for production AI telemetry
Decide what evidence an AI system must produce, which content may be retained, and whether your existing stack, a specialist platform or a hybrid can meet the operating contract.
A security decision for RAG, agents and tool-using AI systems
Treat direct and indirect prompt injection as an authority-design problem. Decide whether to deploy, constrain or reject an LLM workflow with explicit data, tool, approval and evaluation gates.
A deterministic workflow, bounded agent or no-automation decision
Do not buy autonomy for a process that can be expressed and controlled as a workflow. Use this evidence-led framework to choose deterministic code, an LLM-assisted workflow, a bounded agent—or no automation.
A build, buy or bounded-trial framework for one AI workload
Compare a managed model API with a self-hosted open-weight stack on accepted outcomes, full operating cost, data boundaries, control and exit—not token price or infrastructure ideology.
A production decision for knowledge, behavior and combined systems
Retrieval and fine-tuning solve different failure classes. Use this evidence-led decision to choose prompt-only, RAG, fine-tuning, a measured combination—or no LLM change.
A workload evaluation and deployment decision for technical teams
Public benchmarks can shortlist candidates; they cannot decide which configured AI system is acceptable for your workload. This guide turns model selection into a reproducible ship, trial or reject decision.
A production-readiness framework for engineering leaders
A coding agent should earn autonomy through controls and repeatable evidence—not through an impressive demo. This framework turns repository access into a staged engineering decision.
An evidence ladder for technical teams
How to decide whether a research result deserves a prototype, a controlled trial or no engineering time yet.
Measure systems, interventions and failure—not demos
A benchmark design for teams evaluating tool-using agents on their own operational tasks.
A buyer guide for engineering and AI leads
A vendor-neutral scorecard covering evidence, integration, security, economics, operations and exit risk.