Genkit lets teams treat prompt behavior as a project artifact: prompts can live in named files, be loaded and run by application code, exercised in a Developer UI, and evaluated against datasets. That creates practical points for inspection and review. It does not automatically improve output quality or make every runtime decision transparent; those depend on the checks and review process a team builds around it.
What “prompts are code” means in practice
A prompt is part of an application’s behavior when the application sends it to a model. Wording matters, but so can model configuration, input assumptions, output expectations, and values supplied by the caller. Reviewing only the prompt’s prose can miss changes made elsewhere in the call path.
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Genkit’s Go Dotprompt documentation describes prompts stored in .prompt files and loaded by name with genkit.LookupPrompt(), then executed from application code. The same Go sample also demonstrates inline prompts, so a separate file is an option for teams that want a named artifact to inspect—not a requirement. Genkit Go Dotprompt documentation and the Go basic-prompts example show these patterns.
For review, consider both the saved definition and the call site. Genkit documents that execution-time values can override corresponding values in the prompt file. A reviewer should therefore trace which settings the application actually supplies, not assume the file alone determines the request.
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What a prompt file can make explicit
The Go Dotprompt guide shows front matter that can include a model, input schema, and output schema. These details can make assumptions easier to see alongside prompt wording: what inputs are expected, which model configuration is selected, and what shape of output the application anticipates. Schemas help define expectations; they do not by themselves establish that generated output is correct or useful.
Some configuration types come from a provider’s SDK rather than from Genkit itself. That distinction matters when reviewing portability: a prompt may use Genkit-level options and also depend on provider-specific settings. Check the relevant provider SDK whenever a configuration change could affect whether the application works with another model provider.
How to iterate on a prompt with the Developer UI
- Start the UI with the application. The documented workflow runs Genkit’s local Developer UI alongside the app so a developer can try prompt executions.
- Vary inputs and wording or configuration. Exercise representative cases, including inputs that matter to the application’s expected behavior.
- Export a useful change. The UI can export a modified prompt to the project’s prompt directory, giving the team a file to inspect and maintain.
- Review it through the project’s normal process. Exporting does not itself create a source-control commit or grant review approval; teams still need their own change-management workflow.
This loop is useful for iteration, but a successful UI run is not proof that all relevant inputs or failure cases have been covered. The saved prompt, the application’s call-site overrides, and the behavior observed during testing are distinct review surfaces.
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Genkit’s JavaScript evaluation guide describes datasets for Flow, Model, and Prompt evaluation. Prompt datasets can be checked against a prompt’s input schema, and teams can select prompt variants for evaluation and comparison. Schema validation is described as a helper, not a hard barrier: invalid examples can still be saved. Genkit evaluation documentation
The guide documents three CLI commands for evaluation workflows:
eval:flowevaluates a flow using either a JSON input file or a dataset available in the runtime.eval:extractDatais part of the documented evaluation CLI for extracting data.eval:runruns evaluations.
The documentation presents CLI evaluation as useful where the Developer UI is unavailable, including CI/CD environments. Integrating these commands into a team’s pipeline is the team’s responsibility; the documentation does not mean a project automatically runs them in CI.
Choose checks for the question you need to answer
Genkit lists built-in Faithfulness, Answer Relevancy, and Maliciousness evaluators, and supports custom evaluators using an LLM judge, heuristic checks, or external APIs. A score reflects the selected evaluator and criteria. It is evidence about the behavior that was tested, not a universal verdict on prompt quality.
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- Schema compatibility: checks whether an example fits an expected input structure; it does not judge answer quality.
- Evaluator metrics: assess behavior according to the chosen metric and evaluator; they do not cover qualities the metric does not measure.
- Human inspection: helps reviewers assess outputs and context directly, but only for the examples and executions they inspect.
What traces and monitoring add
Genkit’s project page describes using the Developer UI to inspect detailed traces of past executions and review evaluation results with links to relevant traces. That can help connect an evaluation outcome to the execution details behind it. The same page describes production monitoring for model performance, request volume, latency, and error rates. Those operational signals help identify runtime issues, but they are different from evaluating whether an answer meets a product’s quality expectations. Genkit project page
A practical review checklist
- Is the prompt kept inline or in a named file, and is that choice clear to reviewers?
- What input and output expectations are encoded in the prompt or application?
- Does application code override any values in the saved prompt?
- Are the examples representative of the cases the application needs to handle?
- Which check is being used—schema validation, a selected evaluator, or human review—and what does it actually establish?
- Can a reviewer inspect traces for executions associated with concerning evaluation results?
- Does configuration depend on a provider-specific SDK, and could that affect portability?
- If repeatable checks are required, are evaluation commands integrated into the team’s own CI/CD process?
Does Genkit require Google Cloud?
No. Genkit’s project materials describe deployment to Cloud Run and other compatible platforms, making Google Cloud one option rather than a requirement. Hosting choice is separate from whether a team uses prompt files, the Developer UI, evaluations, or traces. Genkit project page and Genkit repository
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