Elicitation gap is a software engineering gap between the requirements, capabilities, or preferences that exist and the information a team or evaluation actually captures. It occurs when interviews, documentation, prompts, or tests produce an incomplete, distorted, or overly simplified picture. In practice, the term applies both to software requirements discovery and to AI model evaluation, where the method of asking can change the answer obtained.
Elicitation Gap
What is an elicitation gap?
An elicitation gap is the difference between what a software system needs to do, or what an AI model can do, and what a team actually discovers and records. It appears when interviews, prompts, documents, or tests capture only part of the real requirement, behavior, constraint, or preference.
The missing information is often implicit. A user may say an approval workflow needs a manager sign-off, but not mention delegated approvals, rejected requests, or audit logs until the application is already built.
Elicitation gap in software requirements and AI evaluation
The same idea applies to two technical activities, although the evidence collected differs.
| Context | What is missed | Result |
|---|---|---|
| Software requirements | Rules, exceptions, integrations, or user roles | Incorrect user flows and rework |
| AI evaluation | Capabilities or preferences obscured by a narrow prompt | Overconfident conclusions about model behavior |
How an elicitation gap happens
Gaps commonly emerge through a predictable chain of simplifications.
- An important assumption remains unstated.
- A question or prompt limits the possible response.
- Documentation converts ambiguity into a seemingly fixed decision.
- Implementation or testing exposes the mismatch, such as an approval request submitted by a delegated manager.
Elicitation vs. requirement gathering
Requirement gathering collects stated inputs. Elicitation goes further by uncovering and testing what has not yet been stated.
| Activity | Main purpose | Typical output |
|---|---|---|
| Gathering | Record requests and existing documentation | Feature list |
| Elicitation | Clarify assumptions, constraints, and exceptions | Validated scenarios and acceptance criteria |
Benefits of finding the gap early
Early discovery improves the quality of both software delivery and AI assessment.
- Clearer acceptance criteria and stronger automated tests.
- Fewer avoidable rework cycles after development starts.
- More reliable flows for unusual but valid user actions.
- Safer interpretation of AI evaluation results.
Limits and risks to manage
No single interview, prototype, or prompt can reveal every relevant detail.
- Different users may describe the same workflow differently.
- System behavior can change with role, data state, or integration failures.
- AI responses can be sensitive to wording and available answer choices.
- Confidence should match the scenarios and evidence actually tested.
How to reduce an elicitation gap
Use an iterative process rather than treating an initial request as complete.
- Combine interviews, documentation review, and observation of current workflows.
- Ask scenario questions about errors, permissions, edge cases, and failure recovery.
- Review a prototype before treating a requirement as final.
- Write testable acceptance criteria for each important behavior.
- In AI evaluations, allow neutral or abstain responses and vary prompts.
- Validate stated requirements against observed system or user behavior.
Elicitation gaps when building with Emergent
When building with Emergent's AI app builder, a precise natural-language specification is a useful starting point, not a final contract. Review early prototypes, add acceptance criteria, and use follow-up prompts to expose missing rules before deployment. Guidance on app design and development can help turn broad ideas into testable flows, while an AI agent builder project benefits from explicit boundaries for what an agent may decide or automate. Build better-specified AI apps with Emergent.
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