Logging the model version is a brilliant, painful addition. I got burned by that exact scenario. Elicit rolled out a "improved" extractor for a key field, and suddenly my last six months of historical charts looked like the model performance had magically doubled overnight. It was just a change in what they considered a valid match for the extraction prompt.
Your chip count correction is the right move. I'd add that you also need to watch for papers reporting "system throughput" on a cluster versus "chip throughput". That's a manual flag I have to add after the fact, because Elicit won't catch the distinction. So my reference table actually has two coefficients: one for single device, one per system, based on that flag.
It's a constant battle between automation and manual oversight. You automate the 95% routine extraction, then spend your time cleaning up the 5% edge cases the automation will never get right.
APIs are not magic.
Cool setup, but you're trusting Elicit's CSV extractions way too much.
That accuracy/throughput ratio formula is gonna bite you when someone reports latency instead of throughput, or FLOPS, or uses a different accuracy metric. Your derived field will silently break on the next batch of papers.
The "single source of truth" is only true until your extraction misses a critical footnote. Then your dashboard is just a pretty lie. Been there.
That's a scary point. I've caught Elicit swapping "latency" for "throughput" before and it completely broke my trending view.
So how do you build a safety check for that? Do you manually spot-check every paper's key metrics, or is there a script trick to flag mismatched units? Seems like a huge manual lift either way.
Oh, the accuracy/throughput formula is just the tip of the iceberg. The unit mismatch problem is a guaranteed failure mode.
My safety check is a validation middleware step that runs before the Airtable sync. It's a simple rules engine that flags records where extracted values don't pass sanity checks. For example:
- Throughput should be a positive number; latency probably isn't 10,000 samples/sec.
- Accuracy metrics are bounded between 0 and 1 (or 0-100). A value of 92.4 is fine; 1,024 is a unit error.
It doesn't catch everything, but it throws a row into a "quarantine" table for manual review instead of polluting the main dataset. The silent break becomes a noisy, blocked pipeline. It adds overhead, but less than rebuilding a corrupted dashboard.
APIs are not magic.
The traceability benefit is exactly why I'd expand that Elicit query log to include the full extraction prompt used. You're capturing the 'what' with the CSV, but the 'why' and 'how' of the data's shape lives in that prompt text. A change from "extract the throughput" to "extract the throughput in samples per second" can be the difference between clean data and a unit mismatch catastrophe.
Your simple Python script is the perfect place to inject a validation layer, as others have noted. But instead of just a quarantine table, consider versioning your entire Airtable schema alongside the data. Add a field for the 'extraction schema version' that increments when you change the expected fields or units. That way, your Gantt chart can have a filter to show only data pulled under version 1.2 of your process, saving you from comparing apples to oranges when Elicit or your own logic evolves.
It turns a living review into an auditable one, which is far more valuable when you're trying to track progress over time.
Measure twice, cut once.
I agree with the hybrid model approach in principle, but in practice, Airtable's scripting blocks can become a maintenance bottleneck of their own. They're a proprietary execution environment with limits on runtime and API calls, and debugging is opaque compared to a local Python script.
Your point about ephemeral transformation is critical. My solution is to treat the Python script itself as the artifact of record. It's checked into git alongside a schema manifest, and it logs both the raw CSV and its cleaning decisions to a dedicated `_audit` table in Airtable. This creates an append-only trail without needing a separate staging table for every sync.
The real trade-off is version control. Airtable's automations and scripts are versioned internally by Airtable, which is a black box. When a mapping rule breaks, I need `git diff` to know what I changed last week, not "it worked yesterday." So I keep the core logic in Python, but use Airtable automations only for simple, idempotent tasks like kicking off a sync.
CPU cycles matter
Yeah, that unit mismatch problem sounds like a real headache to catch manually. I'm still getting used to these tools, so this might be a silly question, but how do you even *know* what rules to write for your validation script? Like, how did you figure out throughput "should" be a positive number and accuracy is between 0 and 1?
Do you just have to be a domain expert in the papers you're reviewing to spot those patterns?
The traceability you're aiming for is precisely what breaks down without explicit schema versioning attached to each record. You've mentioned logging the query date, but that only tells you *when*, not *under what conditions* the data was shaped.
When your script inserts a row, it should also write a companion record to a separate `extraction_metadata` table linked to that paper. At minimum, that record should contain the exact Elicit prompt used, the API version or model identifier if available, and a hash of your own cleaning script. This turns your Airtable base from a static "source of truth" into an auditable ledger. You can then filter your Gantt chart by, say, "all data extracted using prompt schema v1.2," which isolates changes in your extraction logic from actual trends in the literature.
Otherwise, a year from now, you won't know if a spike in your accuracy/throughput ratio is a genuine research breakthrough or just the day you tweaked the CSV parsing to handle a different unit notation.
Measure twice, cut once.
That's a solid foundation for a living review. Your three-step workflow mirrors how I'd start, but I'd immediately add a fourth step you're hinting at: a structured validation layer.
Your derived formula fields are a major risk point, as others have noted. A formula calculating accuracy/throughput will propagate garbage if an extracted "throughput" value is actually latency in milliseconds. I'd build those validation rules directly into your Python script before the `airtable.insert` call. At minimum, check that:
- Numerical fields are within plausible bounds (accuracy <= 1 or <= 100).
- Throughput is a positive number; flag any value over, say, 10,000 for manual review as it's likely a unit error.
- Required fields like model name aren't empty.
This creates a quarantine process instead of corrupting your single source of truth. The script should log each flagged record with the reason to a separate `_validation_issues` table.
Also, are you logging the Elicit query itself as a field on each record? Without that, you lose traceability when you refine your search terms later.
Measure twice, buy once.
This is a really smart way to build that traceability you mentioned. I'm setting up something similar for marketing campaign literature, and I was wondering about tracking the extraction logic itself.
> The Airtable base becomes the single source of truth.
Couldn't this still drift if you tweak your Python cleaning script over time? If you change how you clean "model name" in month two, the older records aren't updated. Do you run your new cleaning logic retroactively on the old data in Airtable, or do you just accept that the "source of truth" evolves?
That's a good foundation, but your step 3 - Airtable as the single source of truth - is a common and dangerous oversimplification. Airtable is your curated, *derived* dataset, but the source of truth is the original paper PDFs and your raw extraction CSVs.
The formula fields in your base are a perfect example of this risk. You mention calculating an accuracy/throughput ratio. If your Python script's cleaning logic changes in six months - say, you start normalizing all throughput to a standard hardware platform - the formula will be applied inconsistently across your historical data. Old records reflect the old logic, new ones the new logic, corrupting your trend analysis.
Your script should embed a schema version tag into every record it inserts. That allows you to segment or backfill data when your extraction methodology evolves. Treat your Airtable base as a materialized view, not the primary source.