# Session Analysis: rollout-2026-01-29T23-17-23-019c0c0c-0fec-7e31-b81d-a2b730ad8416.jsonl

**Date**: 2026-01-29 | **Duration**: 48.8s | **Turns**: 4 | **Model**: gpt-5.2-codex
**Type**: codebase-inquiry | **Outcome**: resolved | **Specificity**: 4/5 | **Struggle**: 0/10

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## Journey
The developer was working on a recommendation system / cold-ranker ML project and had the config file open in their IDE with `MODE='test'` selected. They asked a single, focused question: what happens to data usage when switching to `MODE='prod'`? The agent methodically read the config file (after an initial sandbox permission failure), searched for all MODE references across the codebase with `rg`, then read training.py, data_prep.py, and dataset.py to trace how MODE affects the data pipeline. The agent synthesized findings from four files into a clear, file-by-file explanation confirming that prod mode recombines train+test shards for training and skips evaluation.

## Prompting Assessment (4/5)
**What worked:**
> "if i use MODE='prod', will i be using all the data for training?"

This is a well-scoped question. The developer highlighted the relevant line (`MODE='test'`) in their IDE selection, gave full context of which files were open, and asked a specific yes/no question about behavior. The IDE context (open tabs listing config.py, model.py, data_prep.py, training.py) helped the agent know exactly where to look.

**What was vague or could improve:**
The prompt was quite good. A minor improvement would have been specifying what "all the data" means -- whether referring to all raw data, all prepared shards, or bypassing the train/test split entirely. The agent had to investigate whether data_prep.py itself respects MODE to fully answer.

## Outcome: resolved
**Evidence:**
> "all data in `../../data/{LANGUAGE_KEY}/train` + `../../data/{LANGUAGE_KEY}/test` is used for training, and you won't get a held-out test loss in `prod`."

The agent gave a comprehensive, file-by-file breakdown and even proactively noted the subtlety: data_prep always splits, but prod mode recombines at training time. It also offered two actionable follow-ups for different developer intents.

## Struggle Points
There was no meaningful struggle in this session. The only minor friction was the sandbox permission error on the first tool call:

> "error running landlock: Sandbox(LandlockRestrict)"

The agent immediately recognized this and re-issued the command with escalated permissions. The reasoning trace shows quick adaptation:

> "The tool failed to read due to Landlock sandbox restrictions, indicating escalation is needed to permit reading access."

This is a platform/environment issue, not a user or agent struggle.

## Aha Insight
The key insight the agent surfaced was the asymmetry between data preparation and training: MODE does not affect how data is split, only how it is consumed.

**Evidence:**
> "In `col/src/data_prep/data_prep.py`, the split still happens (via `TRAIN_TEST_SPLIT_RATIO`), but `MODE` is not used there -- `prod` just *recombines* those splits for training."

This nuance matters because a developer might assume MODE='prod' prevents the split entirely, when in reality the split still happens and prod just merges both halves back at training time. The agent even offered the real fix if the developer wanted to eliminate splitting: "you'd need to change `TRAIN_TEST_SPLIT_RATIO` in `col/src/config/config.py` and re-run data prep."

## Coach Tip
This session was a model example of using AI for codebase comprehension. One specific improvement: when asking about config flag behavior, consider explicitly listing the files you think are affected so the agent can confirm or correct your mental model. For example: "Does MODE='prod' change behavior in data_prep.py, training.py, and dataset.py? Does it use all data for training?" This turns a yes/no question into a verification of your understanding, which surfaces gaps faster.
