How to Assess AI Model Provenance and Capability Boundaries
Not all AI models are built the same way. Frontier models develop deep reasoning structures through massive compute investment; distilled models compress those capabilities into cheaper packages that can fail unpredictably on complex tasks. This playbook gives you concrete techniques for investigating model origins, evaluating vendor claims critically, and mapping where specific models break down in your domain so your routing decisions are grounded in evidence rather than marketing.
This playbook covers the how. For the why and what, see the
skill definition
.
Developing Start here. Build the foundation.
- For every AI model you currently use, spend 15 minutes researching its training origins. Check the vendor's technical documentation, blog posts, and model cards to determine whether the model was trained independently or derived through distillation from a larger model. Create a one-line summary for each: model name, training method (frontier/distilled/fine-tuned), and vendor. Keep this in a reference document you update whenever you adopt a new model. Knowing what you are working with is the foundation of every routing decision.
- Run a simple comparison test to see benchmark limitations firsthand. Take a task from your real work that requires multi-step reasoning, give it to both a frontier model and a budget or distilled model, and compare the outputs side by side. Note where the budget model matches the frontier model and where it fails. Do this with 3 different task types. The pattern you observe, strong performance on narrow tasks and degraded performance on complex reasoning, is the core insight that benchmarks hide because they over-index on narrow evaluations.
- Start a 'model behavior log' where you record surprising model failures or unexpected capability gaps as you encounter them in daily work. Use a simple format: date, model name, task description, expected behavior, actual behavior. After collecting 10 entries, review them for patterns. You will likely find that failures cluster around specific task types or reasoning demands, and that information directly informs your routing decisions.
Proficient Build consistency and rhythm.
- When a vendor releases a new model or updates an existing one, apply a structured evaluation before changing your routing. Check three things: (1) What training methodology was used and how does it compare to the previous version? (2) What do independent evaluators (not the vendor) report about real-world performance? (3) How does it perform on your domain-specific test cases? Do not adopt based on announcement hype. A 15-minute structured check prevents routing changes that look good on paper but degrade production quality.
- Map capability boundaries for every model you route tasks to in your specific domain. Run each model through 5 tasks that span the difficulty spectrum in your work: one trivial task, two moderate tasks, and two that push the model's limits. Record where each model transitions from reliable to brittle. Update this map quarterly. The boundary lines, not the peak performance numbers, are what determine safe routing thresholds.
- Build a one-page capability comparison that contrasts what vendor documentation claims versus what your testing reveals. Organize it by task type, not by model. For each task type, list which models are adequate, which are marginal, and which are inadequate based on your evidence. Share this with colleagues who make model selection decisions. Vendor-independent capability maps prevent procurement decisions based on the most compelling sales pitch rather than the most reliable production performance.
Mastered Operate at the highest level.
- Prepare a provenance risk briefing for leadership that covers three scenarios: (1) what happens if your organization routes critical workflows to distilled models that appear equivalent on benchmarks but fail on complex production tasks, (2) what the cost differential looks like between model tiers for your actual workload, and (3) what procurement guardrails would prevent over-reliance on models with unverified provenance. Use real examples from your capability mapping to make each scenario concrete. Deliver this when budget or procurement discussions arise.
- Establish a model provenance review as a standard step in your team's AI procurement process. Before any new model is adopted, require documentation of its training methodology, known limitations, and independent evaluation results. Create a simple checklist: training origin verified, capability boundaries mapped for your domain, comparison against current models completed on real tasks. This prevents the pattern where teams adopt models based on benchmark scores and discover production failures after deployment.
- Mentor one or two colleagues on provenance assessment by walking them through your evaluation process on a model they are considering. Show them how to find training methodology information, how to interpret model cards and technical reports critically, and how to design a quick capability test for their specific domain. The goal is building this assessment capability across the team rather than concentrating it in one person, so routing decisions remain evidence-based even when you are not involved.
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