Hey folks! 👋 I was working on a systematic review for an internal post-mortem report, and it got me thinking: how do we ensure our literature review process is as observable and reproducible as our production systems? We use tools like Datadog for tracing code, so why not apply similar principles to academic or evidence-gathering work?
I used **Elicit** to help with the literature search and screening, but I wanted to create a clear audit trail. Here’s a quick walkthrough of my makeshift "observability pipeline" for the review.
**My core requirements were:**
* Every search query and its result count must be logged.
* The inclusion/exclusion decision for each paper, with a reason, must be recorded.
* All notes and extracted data should be versioned.
* The final list of included papers should be exportable with all its metadata.
I started by documenting my process in a structured note-taking app (Obsidian, but you could use Notion or even a spreadsheet). The key was treating each step like a span in a trace!
**For example, I logged my Elicit searches like this:**
```yaml
Search_Date: 2023-10-26
Query: "llm agent reliability engineering fault tolerance"
Platform: Elicit
Filters: Year > 2018, Include Abstracts
Results_Count: 127
Saved_to_CSV: elicit_search_llm_reliability_20231026.csv
```
Then, during screening, I tracked my decisions in a table that linked back to the source search. It looks a lot like adding custom attributes to a log line.
| Paper Title | Search Source | Decision | Reason | Date |
|-------------|---------------|----------|--------|------|
| A Study on LLM... | elicit_search_llm_reliability_20231026.csv | Include | Directly addresses evaluation metrics | 2023-10-27 |
Finally, I exported everything from Elicit and my notes, and used a simple Python script to merge the CSVs and generate a final report. This gave me a complete "trace" from initial question to final evidence base.
Has anyone else tried to build a reproducible workflow for literature reviews? I'm curious if there are better tools or scripts out there to automate this audit trail. It made defending my conclusions during the report review so much easier!
Dashboards or it didn't happen.
Absolutely love this analogy. We obsess over trace context and span hierarchies in our APM tools, then treat literature reviews as a black box. The structured logging you're doing is essentially manual instrumentation for your research pipeline.
I'd push the analogy a bit further. You're logging the search queries, but what about the "sampling rate"? In Elicit, your results depend heavily on the underlying models and databases it queries at that moment, which can drift. A search repeated next month might yield different papers. You'd need to log Elicit's own configuration or version if possible, similar to logging a library dependency.
The inclusion/exclusion decision with a reason is your "business logic," and capturing that is key. Have you considered a lightweight tagging system for exclusion reasons? It would let you run quick aggregates later - e.g., "75% of exclusions were due to insufficient sample size," which is like a dashboard for your review's health.