Your first Gemini 4 Argon debugging lap
A bug rarely arrives as a tidy question. You might have a screenshot of a broken form, a console error, and a teammate’s description of what happened. Multimodal debugging brings different kinds of evidence into one conversation. The goal is not to let a model declare the answer. It is to produce notes that help a developer reproduce, investigate, and verify the problem.
For a first run with Gemini 4 Argon, keep the task narrow: one bug, a small evidence bundle, and a requested note format. Before attaching images, confirm that your selected endpoint and client support image input for the model. A model name alone does not guarantee that every integration accepts the same input types.
Pack one bug into a small evidence bundle
Start with a screenshot showing the visible problem, relevant logs copied as text, and the smallest useful code excerpt. Add the steps that led to the failure. Text logs are easier to quote precisely than tiny characters inside a screenshot, while the image can preserve layout and visual context.
- Expected behavior: Explain what should have happened after the user’s action.
- Observed behavior: Describe the visible failure without guessing its cause.
- Reproduction steps: List the actions, test data, and environment needed to trigger it.
- Evidence labels: Name inputs clearly, such as Screenshot A, Console Log B, and Form Handler C.
- Scope: Include the app version, browser, and relevant recent change when known.
Remove API keys, session cookies, customer details, and private URLs before sending anything. Crop screenshots to the relevant area, but preserve enough surrounding interface to explain what the user was doing.
Ask Gemini 4 Argon to separate evidence from guesses
A useful starter prompt is more specific than “debug this.” Tell the model what each attachment represents and require it to distinguish direct observations from possible explanations. That separation makes the resulting notes easier to trust and review.
Starter prompt: “Create debugging notes from Screenshot A, Console Log B, and Form Handler C. The user submits a valid form, but the page displays an error. Separate observed facts, hypotheses, missing information, and next checks. Reference evidence labels for each factual claim. If text is unreadable or evidence conflicts, say so. Do not present a root cause as confirmed unless the supplied evidence establishes it.”
This prompt creates a useful boundary. A screenshot might show an error banner, but it cannot establish whether the server rejected a request. A console message might identify a failed request without explaining the underlying cause.
Turn the response into reproducible debugging notes
Ask for a compact handoff that another developer can use without reading the whole conversation. Keep these sections in the requested output:
- Reproduction: The steps and conditions supplied by the reporter.
- Observed evidence: What the screenshot, logs, and code actually show.
- Working hypotheses: Possible explanations, each paired with supporting evidence and uncertainty.
- Next checks: Specific inspections or tests that could confirm or reject each hypothesis.
- Verification: What to repeat after a fix, including a nearby failure case.
For example, if the model suspects response parsing, the next check should be concrete: inspect the response status and body, then compare them with the parser’s expected structure. “Check the backend” is too broad to be a useful debugging instruction.
Connect through SwiftRelay and verify the handoff
SwiftRelay is a practical option for pay-as-you-go, per-token access to Gemini 4 Argon. Sign up for SwiftRelay to try this workflow with the small gift credit available to new accounts. Use https://api.fastapirelay.com as the bare host for Gemini CLI; OpenAI-compatible SDKs use https://api.fastapirelay.com/v1. Check the supported model identifier and image-input format before sending your evidence bundle.
Finish by reviewing every factual claim against the original inputs. Run the proposed checks yourself, record their results, and update the notes. The useful deliverable is not a confident-sounding diagnosis: it is a reproducible bug report with clearly marked uncertainty and a testable next step.