AI & LLMs
AI Assets: Organizing Data, Models, and Evaluation Evidence
Treat an AI workflow as a connected collection of data, configuration, tests, and decisions. Learn what to record and how to maintain it.
AI & LLMS
AI Cyber Assets: Know What Your Workflow Uses
A useful AI workflow has a history: source information, preparation choices, instructions, configuration, and evidence from testing. Connect those pieces so you can explain a result and assess what should happen when something changes. Make that context available to the next maintainer.
Understanding ai assets
Start with a bounded task and the people involved. Identify what enters the workflow, what comes out, and who reviews the result. This provides a practical boundary for deciding which files, settings, and records deserve attention. Include the moments where someone supplies missing information, corrects an output, or decides that the task needs another route.
The guide to AI assets, data, models, and evaluation develops this approach in detail. Use the page below as a planning surface: identify the components, connect their responsibilities, and make unresolved questions visible before the workflow expands. Keep a concise explanation of why each component matters, so the inventory helps with a decision rather than simply naming files.
Define the intended use
Write the task, audience, input, output, and review step in terms a maintainer can recognize during everyday work.
Trace the source material
Connect prepared inputs with their originals, applicable permissions, transformations, and known gaps so questions can be investigated.
Identify the working configuration
Record the instructions, model identifier, relevant settings, and supporting services used for an approved version of the workflow.
Preserve evaluation evidence
Keep the cases, judging criteria, observed failures, and approval decision together so another reviewer can understand the conclusion.
Make the topic practical
A practical map of the workflow
| Ask yourself | A useful next step |
|---|---|
| Where did this information originate? | Follow the source reference through preparation and into the material supplied. |
| Which instructions shaped the response? | Record the prompt version alongside its variables and expected output structure. |
| What supported approval for use? | Connect the tested configuration with evaluation results and remaining limitations. |
| Who handles a reported problem? | Name the maintainer and describe the route for review or fallback. |
Continue the exploration
Further reading.
AI & LLMs
An LLM Model Selection Checklist for Practical Projects
Choose an LLM by the work it must perform. A practical checklist for evaluating model evidence, deployment choices, and ongoing costs.
Common questions
AI assets,
explained.
Is an AI asset always a model?
Use the term broadly enough to include the material your workflow depends on. Source collections, instructions, configurations, and evaluation records may each need separate attention. The right boundary follows the task you are maintaining.
What if the model is hosted elsewhere?
Record the service configuration and access arrangements you actually use. Keep credentials outside the inventory, while documenting who can manage access and review changes. Identify what must be reviewed if that service changes.
How much documentation is enough?
Start with records that answer practical questions about origin, behavior, changes, and responsibility. Add detail when a real review or handoff reveals a missing connection. Someone unfamiliar with the setup should understand its intended use.
Make your next step an informed one.
Find a field guide for the part of your project you are working through.