Practical guide · · By Raz Dagan
How to roll out an LLM code replacement with a fallback and rollback plan
Plan shadow comparisons, explicit fallback conditions, a limited release and rollback before replacing a production LLM transformation.
Define the boundary you are changing
A replacement can pass an offline evaluation and still need careful integration. The next question is operational: which requests will reach the function, what happens outside its supported input contract, and how will you return to the previous path? Write those decisions before you merge a production change. This guide describes an application rollout plan, not a claim that ModelGate operates that rollout for you.
Choose one transformation at one call site. Record the accepted input shape, output schema and downstream consumer. If the original request also reads current account state or resolves ambiguous instructions, do not quietly remove those responsibilities. Put the intended boundary in the pull request so the reviewer can compare the replacement with the behavior that actually matters.
Keep offline acceptance separate from release decisions
Evaluate the fixed candidate on held-out examples that were not used to construct it. Decide which differences count as failures, including invalid output and missing fields. Keep difficult cases in the report rather than hiding them inside an overall agreement percentage. A model answer can itself be wrong, so agreement with it is not proof of business correctness.
ModelGate documents a reviewed code-replacement workflow based on recorded examples. Its verified code runs in your application; Python versions are ports that ModelGate does not execute. Review the generated implementation and its dependencies before deciding how to deploy it. A displayed match rate does not authorize every language port, input variation or future change.
Compare without changing the customer result first
Where your architecture permits it, run the candidate alongside the existing path and continue returning the existing result. Compare the two outputs using the acceptance rule you defined. This is an optional shadow stage you implement in your application. Check the cost and privacy implications before copying requests into another execution path.
For a fictional webhook transformation, compare required keys, value types and the meaning of an absent value. A string containing zero and a numeric zero may look similar in a log but behave differently downstream. Avoid triggering side effects twice: shadowing a transformation should not create a second ticket, send another notification or update a customer's record.
Make fallback conditions explicit
Define when the replacement is allowed to answer and when the existing path remains responsible. Possible conditions include an unsupported schema version, a required field that is absent, a parsing error or a dependency failure. These are examples for designing your own contract, not universal reasons to call a language model. Sometimes rejecting invalid input is the correct behavior.
Distinguish unsupported input from an unexpected bug. Record a bounded reason code rather than full customer text. Set a timeout appropriate to the surrounding service and decide what the user should receive if both paths fail. An exception handler that returns an empty successful result can conceal data loss and should not become the default fallback.
Release to a limited, identifiable slice
Start with a deliberately selected portion of eligible traffic if your deployment controls support it. Keep the candidate version and routing decision identifiable so comparisons refer to the same implementation. Choose the release size from the consequence of a wrong answer and the volume needed to inspect meaningful cases; there is no universally safe percentage.
Compare supported-case errors, invalid outputs, fallback frequency and response time with the previous path. Track actual provider use as well: a replacement that frequently falls back may save less than its nominal per-call comparison suggests. Include duplicate shadow calls and operational work when assessing the trial. Do not present a synthetic example as measured customer savings.
Write the rollback action before it is needed
Name the person who can disable the replacement and specify the actual switch or deployment procedure. Confirm that the old path still accepts the relevant input and that credentials and dependencies remain available. A rollback instruction that depends on finding an old prompt in someone's laptop is not a usable operational plan.
Keep a short release record: candidate version, input contract, evaluation set version, release time, observed issues and the decision to expand or stop. Investigate a failure before regenerating the function against it, then preserve fresh held-out cases for the next evaluation. Use ModelGate's documentation and demo to assess a candidate; retain responsibility for review, deployment and monitoring in your application.
Explore the workflow
Read the code-replacement requirements, GitHub workflow and gateway comparison before deciding.