A tiny prompt journal can block an accidental repeat when the request you are about to send is structurally unchanged from the last one. It parses the request as JSON, serializes it in a consistent form, hashes that form, and compares it with a saved receipt. This detects changes in request data—not meaning, experimental quality, or whether a model would answer the same way twice.
What an “empty diff” means here
The journal keeps a receipt for the last request object. On a later run, it compares the new request with that saved object. If none of the compared top-level fields changed, the journal reports an empty diff and blocks the send. If a field changed, it reports the changed key and permits the request to proceed.
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This is a guard against sending the same request again by mistake. It does not call a model, establish that two prompts are semantically equivalent, or decide whether a changed request is a scientifically meaningful new trial.
How the request fingerprint works
- Parse the JSON. The script rejects malformed JSON and checks that the top-level value is a Python dictionary. JSON itself also allows arrays and scalar values, so that dictionary requirement is the script’s own input contract. See the Python 3.14.8 JSON documentation.
- Serialize consistently. The tutorial’s
canonicalizefunction callsjson.dumps(request, sort_keys=True, ensure_ascii=False, separators=(",", ":")). Sorting keys makes object key order consistent; compact separators remove formatting whitespace.ensure_ascii=Falseleaves non-ASCII characters unescaped in the serialized string. - Encode and hash. The script encodes that string as UTF-8, then passes the bytes to
hashlib.sha256to produce a digest. Python documents the hash interface in its hashlib documentation. - Compare and record. The journal saves the request and digest. When a prior entry exists, it compares values for the union of the old and new top-level keys, then records the previous digest as
changed_fromif the request has changed.
Because the script parses and serializes the JSON before hashing, different key order or insignificant JSON formatting whitespace should not by itself count as a change, provided the parsed values are the same.
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Build and run the small utility
The tutorial specifies Python 3.9 or newer and no third-party packages. Its implementation uses Python’s standard-library json, hashlib, sys, and pathlib modules. The journal is an input-checking and comparison step for your own workflow; it does not send a model request.
Use the tutorial’s script and fixture files as the implementation, then run each request through the journal before handing it to whatever sends your model call. Keep the request object—the fields actually sent—as the input. Hashing an informal note about a request instead would not protect against changes to the real request.
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What the three fixtures demonstrate
| Run | Request state | Expected journal outcome |
|---|---|---|
| First contact | No journal entry exists yet. | action: send; there is no previous request to compare. |
| Repeat | The same fixture is submitted again. | action: block, reason: empty diff, an empty changed-key list, and exit code 2. |
| Edited instruction | A tie-break sentence is added to instruction. |
action: send; the changed-key list contains instruction. |
These are the tutorial’s expected traces, not independently reproduced test results. A request with the same values but a different JSON object key order should also block after parsing and consistent serialization.
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It detects representation changes, not meaning
An exact comparison can classify a paraphrase as a change even if it means the same thing. It can also report a changed field without judging whether that change matters to the experiment. The question “If the canonical request did not change, what new evidence do I think a model is going to hand me?” is a useful prompt for deciding whether a retry has a reason; the journal cannot answer it for you.
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Changed-key reporting is shallow
The sample reports changed top-level keys. If a nested object changes, it is compared as a whole rather than reported as a path to the nested value. Treat the output as a compact indication of which request fields differ, not a detailed semantic diff.
Be careful about normalizing
Consistent serialization removes irrelevant differences in JSON key order and formatting. Do not normalize away changes that affect model-facing content, and do not substitute a note or label for the actual request. The comparison is only useful to the extent that its input represents what you intend to send.
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Choosing the guardrail behavior
- Parsed, sorted JSON versus raw-input hashing: the tutorial uses parsed request data serialized with sorted keys and compact separators, so key order and formatting whitespace do not create false changes.
- Changed fields versus a yes-or-no result: reporting changed top-level keys gives a little more context about why the request is allowed through, while remaining a structural rather than semantic comparison.
- Block unchanged retries versus allow every retry: blocking is useful when an accidental duplicate is the risk. If a deliberate repeat is meaningful in your workflow, decide how to handle it explicitly rather than treating an empty diff as proof that another attempt has no value.
What to understand when it works
A successful block means only that the compared request fields match the journal’s previous request under this serialization and comparison scheme. A successful send means the sample found a structural change. Neither result evaluates prompt quality, model behavior, or whether an experiment is valid. As the tutorial’s author, Alex Chen, puts it: “The journal never calls a model.”
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