# Session Analysis: rollout-2026-01-28T17-07-55-019c0593-725c-79a2-b506-83ee83448256.jsonl

**Date**: 2026-01-28 | **Duration**: 10s | **Turns**: 7 | **Model**: gpt-5
**Type**: bug-fix | **Outcome**: abandoned | **Specificity**: 4/5 | **Struggle**: 6/10

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## Journey

The developer had just completed a 17-epoch training run of their cold-ranker model (a Bengali language classification model using PyTorch on CUDA). The training output showed classic overfitting: train_loss steadily decreased from 0.2603 to 0.1905, while test_loss increased from 0.2579 to 0.3313. The developer asked "how to improve my model?" but interrupted the assistant almost immediately (within ~3 seconds) before it could provide an answer.

## Prompting Assessment (3/5)

**What worked:**
The developer provided the full training log with epoch-by-epoch metrics, which is excellent raw material for diagnosing model issues.

> "Epoch 001 | train_loss=0.2603 | test_loss=0.2579 | elapsed=155.0s\nEpoch 002 | train_loss=0.2519 | test_loss=0.2589 | elapsed=153.2s\n...\nEpoch 017 | train_loss=0.1905 | test_loss=0.3313 | elapsed=153.9s"

**What was vague or could improve:**
The question "how to improve my model?" is extremely broad. The training logs scream overfitting, but the developer didn't specify what "improve" means — lower test loss, prevent overfitting, faster convergence, or something else. A more targeted prompt like "my test loss is diverging from train loss after epoch 3, how do I fix overfitting?" would have been far more actionable.

> "how to improve my model?"

## Outcome: abandoned

**Evidence:**
> "I'll quickly scan your training code and configs to give targeted improvements."

The assistant started to investigate but the developer interrupted before any answer was provided. The `turn_aborted` event confirms this was a deliberate interruption.

## Struggle Points

The developer interrupted the session almost instantly — the entire session lasted only 10 seconds. This suggests impatience or a realization that the question needed to be asked differently. The assistant hit a sandbox error on its first `ls -la` command and couldn't even begin reading the codebase.

> "The user interrupted the previous turn. Do not continue or repeat work from that turn unless the user explicitly asks."

## Aha Insight

The training data tells a clear story: overfitting begins at epoch 3 (test_loss starts rising while train_loss keeps dropping). By epoch 17, the generalization gap is massive (train: 0.1905 vs test: 0.3313). The developer had the diagnostic data right in front of them but asked a generic question instead of naming the specific problem. Learning to read your own training curves would make every AI-assisted debugging session faster.

**Evidence:**
> "Epoch 003 | train_loss=0.2466 | test_loss=0.2578" — last epoch where test loss was near train loss
> "Epoch 017 | train_loss=0.1905 | test_loss=0.3313" — final epoch showing 74% higher test loss than train loss

## Coach Tip

When you see diverging train/test loss curves, name the problem in your prompt: "My model is overfitting — test loss starts rising at epoch 3. Here's my training config and architecture. What regularization should I add?" This turns a vague 5-minute conversation into a precise 30-second answer.
