Build the safeguards
Eight explained steps, a source example and a checkpoint at each decision.
Read the tutorialPython / FastAPI / AI evaluation
Compare the choices.
Choose a pattern by the failure it addresses. Several safeguards can be necessary at once.
Which approach fits the problem?
| Approach | Useful when | Decision or limitation |
|---|---|---|
| Prompt instructions | Describe desired model behavior | Do not enforce a schema or guarantee source support |
| Structured-output validation | Enforce shape and field constraints | A well-formed claim can still be false |
| Citation validation | Check source identity and applicability | Citation presence alone does not establish entailment |
| Narrow deterministic policy check | Validate a bounded structured domain | Does not generalize to arbitrary prose |
| Regression fixtures | Reproduce known failures cheaply | Do not estimate performance on a representative population |
| Live provider evaluation | Measure actual model behavior and usage | Needs controlled cases, recorded configuration and measured cost |
Which environment should you use?
| Environment | What you can do | Scope |
|---|---|---|
| This browser lab | Click, compare, reset and inspect exact source | Actual Python runs in the open-source Pyodide runtime. Candidate answers and documents are deterministic fixtures; no live model is called. The exact policy validator is intentionally narrow and does not establish general factual accuracy or comprehensive prompt-injection resistance. |
| Independent download | Run the same prebuilt lab with Python’s local server | No package installation or automatic external calls needed for the prebuilt preview |
| Native exercise | Run language tests and the included local adapter or script | Requires the documented local toolchain; does not provision a cloud service |
A comparison you can reproduce
The safeguarded evidence run matches all five expected fixture decisions. The slow run limits active tasks to two and cancels all five before returning. Active-at-finish is zero. These labels are regression cases, not an accuracy estimate.
What this adds to a conventional tutorial
Begin with an observable outcome, inspect the decisions that produced it, then test a repair. The companion tutorial explains the mechanism; the how-to guide takes the same source into an independent environment.
Read the primary documentation
Ascertain describes Python/FastAPI engineering for its healthcare AI platform; this evaluation curriculum is a proposed extension of that work.
Choose your next step
Compare the alternatives
Understand where each pattern helps and which tradeoffs remain.
Compare approachesRun and extend it locally
Download the independent environment, run the native checks and try a specific extension.
Open the how-to guide