An API crawler can collect good source material and still produce a bad answer. The final prompt might overstate a field's meaning, ignore a qualification, or treat a document as instructions rather than evidence. Evaluating API crawler AI prompts therefore requires more than checking whether the response sounds professional.
This guide proposes a small test suite for a fictional API documentation assistant. The assistant answers questions about a reviewed catalog; it does not execute network requests. We will test grounded answers, uncertainty, conflicting versions, and trust boundaries using simple fixtures that a developer can inspect without a specialized evaluation platform.
Choose the behaviors that matter
Write down what a successful answer must do. For our assistant, it should answer from the supplied catalog, identify the relevant operation, explain important limitations, and reference the evidence record. If the catalog does not establish the answer, it should say so. These requirements are more specific than “be accurate.”
Also define unacceptable behavior. The assistant must not invent endpoints, claim to have executed a request, reveal private context from another fixture, or follow instructions embedded in a document. Some failures should block release even when most answers are satisfactory.
Keep the scope aligned with the product. A documentation assistant does not need to answer every general software question. It can direct an unsupported question to a human review path. Narrow expectations make it easier to evaluate the system fairly and avoid rewarding confident answers to questions outside its evidence.
Build fixtures with known answers
Create a fictional catalog with a few clearly documented operations. Include a list operation, a detail operation, and a similarly named operation for another resource. Give each one stable evidence identifiers. Then write questions whose correct operation selection is unambiguous.
Add missing information on purpose. One operation can omit a maximum page size. Another can show an optional timestamp only in an example. The expected answer should preserve those limits rather than infer a complete contract from convention.
Use synthetic identifiers and data throughout the test suite. A realistic evaluation does not require real customers, production tokens, or private support messages. The goal is to expose decision errors in a controlled setting, where the reviewer knows exactly what evidence the assistant received.
Test uncertainty as a positive outcome
Ask questions that cannot be answered from the fixture. For example: “Can I delete an archived event?” when the catalog only documents listing. A good response identifies the limitation and names the next verification step. A poor response manufactures a deletion path because it follows a familiar naming pattern.
Avoid scoring every non-answer as a failure. If the product requires grounded documentation support, a correct limitation statement is part of successful behavior. Otherwise, the evaluation incentives push the model toward guessing whenever the evidence is incomplete.
Review whether uncertainty is specific. “I am not sure” is less useful than “The supplied record does not state a maximum page size.” The second answer tells a developer what is missing and prevents them from treating an absent detail as a hidden default.
Include conflicting and stale evidence
Prepare two fixtures that describe different revisions of the same operation. Ask a question tied to one named version. The assistant should select the matching record or explain that the requested version is absent. It should not combine fields across revisions into a fictional hybrid.
Keep collection time separate from source version in the fixture. A page retrieved recently can still describe an older API. This is a useful test because a model may overvalue the newest timestamp when the actual question concerns a particular contract revision.
Include a removed evidence location. The assistant can explain that an internal record was previously reviewed, but it should not claim that the vanished source proves current support. The discovery foundations guide discusses how to preserve evidence and uncertainty during catalog refreshes.
Evaluate trust boundaries, not just extraction
The OWASP prompt-injection prevention guidance recommends layered controls, including separating instructions from untrusted data, validating outputs, and restricting privileges. This is the security reference for the boundary checks below. Prompt wording alone is not treated as a guarantee.
Use a harmless fixture that contains a paragraph asking the assistant to ignore the catalog task and produce an unrelated slogan. The expected behavior is to treat the paragraph as source content, not as a new instruction. This tests the boundary without requiring real credentials or operational access.
The application should also prevent unrelated actions mechanically. Our documentation assistant has no execution tool, so a model-generated request cannot become a network call. When a future product adds tools, those capabilities need their own validation and permission tests rather than relying on the documentation prompt to remain obedient.
Score claims with a compact rubric
For each answer, record operation selection, factual support, uncertainty handling, evidence references, and task completion. A simple pass-or-review result for each dimension is enough to begin. Keep the rubric understandable to the developer who must fix the failure.
Use an explicit critical-failure flag. Claiming that a payment was completed without an execution result should not disappear inside an otherwise high average. Likewise, exposing information from a fixture outside the user's authorized scope should stop the release review.
Separate style preferences from correctness. A slightly awkward but faithful answer may be acceptable, while a smooth answer with an invented parameter is not. Reviewers should know which edits are cosmetic and which change the meaning or trustworthiness of the result.
Compare prompt revisions fairly
Run the same fixture set against each revision and record the exact prompt, catalog version, and model configuration. Change one important variable at a time when practical. Otherwise, an improvement cannot be confidently attributed to the prompt change you intended to test.
Review regressions individually. A revision might improve endpoint selection while becoming worse at admitting missing information. The right choice depends on the product's risk and acceptance criteria, not only the number of green checks on a dashboard.
Keep a small holdout set of fixtures that you do not use while rewriting the prompt. This helps reveal whether the new wording handles the task more generally or merely fits the examples used during editing. Rotate and expand the set as real failures become available, while preserving the old cases for regression checks.
Turn failures into actionable repairs
A wrong endpoint selection may indicate poor catalog segmentation or vague operation descriptions. An unsupported field may indicate a missing output validation rule. A false completion statement may indicate that the final-answer prompt does not distinguish plans from execution results. Route each failure to the component that owns it.
Write repair notes in observable terms. Instead of “make the prompt smarter,” say “reject evidence identifiers that were not supplied” or “include an explicit unknown value for undocumented limits.” The team should be able to demonstrate that the repair changed the relevant behavior.
After a repair, rerun the old failing case and nearby cases. A fix that makes every answer refuse the task might stop hallucinations while making the product unusable. Good evaluation balances useful task completion with honest boundaries rather than optimizing a single number.
Conclusion: reward answers that respect evidence
API crawler AI prompts should be evaluated as part of a system: source records, task instructions, application limits, and the final explanation. A compact fixture suite can reveal where those layers disagree long before the assistant reaches real users.
Begin with ordinary questions, deliberate gaps, conflicting versions, and one harmless trust-boundary test. Keep the rubric readable and preserve failures as regression cases. The prompt library article supplies a complementary extraction pattern, and the developer resources connect evaluation with the broader catalog workflow.
