# How Nikhil Prompts: Pattern Analysis Across 34 Sessions

## At a Glance

Nikhil prompts through action, not instruction. His dominant pattern is pasting raw terminal output — error tracebacks, training logs, command results — and adding a short question or directive at the end. He rarely describes what he wants in abstract terms; instead he shows the AI what happened and expects it to figure out what to do. His prompts get progressively more directive over the 4-day period, evolving from vague questions ("how to improve my model?") to specific commands ("do what you recommend"). He frequently abandons sessions within seconds, cycling through 2-3 rapid-fire sessions before settling into one that he lets run.

## Opening Moves

Nikhil's first messages fall into five distinct archetypes:

### Archetype 1: "Here's my terminal output" (14 sessions — most common)
He pastes raw terminal output — error tracebacks, pip install failures, training epoch logs — with minimal or no framing text.

> `rollout-2026-01-28T15-51-17`: "(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ pip install wandb\nCollecting wandb\n  Downloading wandb-0.24.0.tar.gz (44.2 MB)... error: subprocess-exited-with-error"

> `rollout-2026-01-29T22-46-03`: "(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ python driver.py\ngetting series meta\nseries vocab len: 1961\n100%|██████████████| 4/4 [00:05<00:00, 1.47s/it]\nWrote shard 0..."

> `rollout-2026-01-30T16-47-16`: "(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ python driver.py\nDevice: cuda\nTraceback (most recent call last):..."

### Archetype 2: "Short question about my code" (8 sessions)
A brief, often vague question with the relevant file open in the IDE.

> `rollout-2026-01-28T20-00-48`: "how can i improve my model?"

> `rollout-2026-01-31T06-14-13`: "can recommend.py be made faster? how?"

> `rollout-2026-01-29T23-17-23`: "if i use MODE='prod', will i be using all the data for training?"

### Archetype 3: "Fix my file" (4 sessions)
Terse imperative with a filename.

> `rollout-2026-01-29T20-30-24`: "can you fix error in model.py"

> `rollout-2026-01-29T20-37-57`: "can you fix error in training.py"

> `rollout-2026-01-29T20-53-38`: "fix my recommend.py"

### Archetype 4: "Build me this feature" (3 sessions)
A feature request, usually with more detail than other archetypes.

> `rollout-2026-01-29T13-21-42`: "i want to now generate recommendations for users. the recommendation should be top 50 contents out of all contents in series meta. We should use example of each user with latest \"at\" with meta features and test with all series meta, get top 50 for each of them."

> `rollout-2026-01-31T07-59-10`: "i actually want to remove series that have already been read by the user, please do the needful. Probably store data during data preperation like series read by the user. and in recommend.py load them to filter out. after filter out recommend top 30 only."

### Archetype 5: "Throwaway / test" (5 sessions)
Sessions abandoned in under 1 second with no meaningful prompt.

> `rollout-2026-01-29T12-06-02`: "hi"

> `rollout-2026-01-29T11-47-50` (second message): "."

## When Things Go Wrong

Nikhil has three distinct correction modes:

### Mode 1: Paste the new error (most common)
When the AI's fix doesn't work, he runs the code, copies the new terminal output, and pastes it without commentary.

> `rollout-2026-01-28T15-57-48`: "(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ unset WANDB_BASE_URL && wandb whoami\nUsage: wandb [OPTIONS] COMMAND [ARGS]...\nTry 'wandb --help' for help.\n\nError: No such command 'whoami'."

> `rollout-2026-01-29T10-18-14` (second training run): "(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ python driver.py\n...Epoch 004 | train_loss=0.2529 | test_loss=0.2590"

### Mode 2: Quote the AI back to itself
He copies a phrase from the AI's own response and says "just do this."

> `rollout-2026-01-28T15-57-48`: "Leave host selection to your shell (hosted vs self-hosted) so your charts open at the correct site\n\njust do this"

> `rollout-2026-01-28T20-00-48`: "Feature crosses: add explicit interactions (element‑wise product and/or learned cross layers) between user_genre_pref_embedding and target_genre_embedding... how to do this?"

> `rollout-2026-01-31T06-25-34`: "Precompute series tensors once, Replace batch topk merge:, Use torch.inference_mode() in score_user - do these"

### Mode 3: Challenge the AI's reasoning
Occasionally, he pushes back on whether the AI's suggestion makes sense.

> `rollout-2026-01-28T20-00-48`: "but does this makes sense? i already do cosine_read_target_genre = F.cosine_similarity(\n            read_series_avg_genre_emb,\n            target_genre_embedding,\n            dim=-1,\n            eps=self.cosine_eps,\n        ).unsqueeze(-1)"

> `rollout-2026-01-31T06-55-05`: "but isnt age_days. normalized as well?"

> `rollout-2026-01-29T20-44-10`: "and how does single worker insure that the negatives are always same for a psoitive?"

### Mode 4: Abandon and restart
He frequently kills the session entirely and opens a new one with the same or modified prompt. This happened at least 8 times across the 34 sessions.

## The Prompt Spectrum

> Minimal: "."
> (`rollout-2026-01-29T11-47-50`, second message — a single period to continue after a long AI response)

> Minimal meaningful: "yes"
> (`rollout-2026-01-31T06-30-50`, `rollout-2026-01-31T06-49-43` — confirming AI's proposed action)

> Maximal: "i want to now generate recommendations for users. the recommendation should be top 50 contents out of all contents in series meta. We should use example of each user with latest \"at\" with meta features and test with all series meta, get top 50 for each of them."
> (`rollout-2026-01-29T13-21-42`)

> Maximal with constraints: "can you break recommend.py into proper functions? also the code assumes backup values if something is not avaialable, remove them and actually throw error if the value is not present. Also all the config values that is required can you keep them at top of the file."
> (`rollout-2026-01-31T08-17-13`)

> Maximal with quantitative target: "currently recommend.py takes 8:47:41 time to run, i want to run in 3 hrs, what changes should id have to do?"
> (`rollout-2026-01-31T06-55-05`)

The typical prompt length is 5-30 words of actual request text, often preceded by hundreds of characters of pasted terminal output or code.

## Evolution Over 4 Days

### Day 1 — Jan 28 (7 sessions): The Vague Explorer
Prompts are broad and unfocused. The same question ("how to improve my model?") is asked 4 times across 3 sessions. He rarely specifies what he wants done.

> "how can i improve my model?" (`rollout-2026-01-28T20-00-48`)

> "how to improve my model?" (`rollout-2026-01-28T17-07-55`, `rollout-2026-01-28T17-08-14`, `rollout-2026-01-28T17-08-38`)

He does show one moment of evolution within a session: selecting a specific suggestion from the AI's list and asking for implementation:

> "Feature crosses: add explicit interactions... how to do this?" (`rollout-2026-01-28T20-00-48`)

### Day 2 — Jan 29 (15 sessions): The Builder
Prompts shift from questions to commands. He starts using imperative verbs more:

> "fix my recommend.py" (`rollout-2026-01-29T20-53-38`)

> "do 1." (`rollout-2026-01-29T20-07-43`)

> "wire them in config with default values" (`rollout-2026-01-29T13-23-37`)

> "do. all the required changes. i want same test set, positive or negative across runs" (`rollout-2026-01-29T20-44-10`)

He also starts providing multi-file context and architectural constraints:

> "col/src/driver.py is my driver file, recommendation should be triggered from this file only not separately, code can be in sepearte file thats fine. Make sure whole code gels togetehre" (`rollout-2026-01-29T13-23-37`)

### Day 3 — Jan 30 (3 sessions): The Debugger
Mostly error-paste-and-fix sessions, plus one about disk space issues. He starts asking clarifying questions before committing to a solution:

> "exhausting permanent memory during write of data prep files - train, test, recommend\nwhat can we do? just tell dont edit code" (`rollout-2026-01-30T13-43-01`)

> "which format is better?" (`rollout-2026-01-30T13-43-01`)

> "is there differnec time in loading and saving of npz and np" (`rollout-2026-01-30T13-43-01`)

### Day 4 — Jan 31 (9 sessions): The Optimizer
Prompts become noticeably more specific and results-oriented. He gives quantitative targets and delegates decisions:

> "currently recommend.py takes 8:47:41 time to run, i want to run in 3 hrs, what changes should id have to do?" (`rollout-2026-01-31T06-55-05`)

> "which is the biggest win? do that" (`rollout-2026-01-31T06-30-50`)

> "do what you recommend" (`rollout-2026-01-31T06-30-50`)

He also provides runtime data as feedback:

> "2479MiB / 15360MiB nvidia-smi" (`rollout-2026-01-31T06-30-50`)

> "3609MiB / 15360MiB" (`rollout-2026-01-31T06-30-50`)

> "Generating recommendations:   0%|                    | 1024/1362277 [00:24<9:06:17, 41.53it/s]\n 3611MiB / 15360MiB\n\nthe it/s or total time are not decreasing even with increasing user_batch_size=32," (`rollout-2026-01-31T06-30-50`)

By Jan 31, he also produces his most structured prompt of the entire dataset:

> "can you break recommend.py into proper functions? also the code assumes backup values if something is not avaialable, remove them and actually throw error if the value is not present. Also all the config values that is required can you keep them at top of the file." (`rollout-2026-01-31T08-17-13`)

## Signature Patterns

### Pattern 1: Terminal-Output-as-Prompt
Nikhil's most distinctive pattern. He copies entire terminal sessions — including the shell prompt, conda env name, full path, and complete output — and pastes them as his message. The actual question is either absent (implied) or a short phrase tacked on at the end.

> `rollout-2026-01-28T15-51-17`: "(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ pip install wandb\nCollecting wandb\n  Downloading wandb-0.24.0.tar.gz..."
> (No question — the error IS the prompt)

> `rollout-2026-01-29T10-18-14`: "(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ python driver.py\n...Epoch 008 | train_loss=0.2489 | test_loss=0.2591\n\n\ntest loss not decreasing"
> (Full training output + short observation)

> `rollout-2026-01-31T06-30-50`: "Generating recommendations:   0%|                    | 1024/1362277 [00:24<9:06:17, 41.53it/s]\n 3611MiB / 15360MiB\n\nthe it/s or total time are not decreasing even with increasing user_batch_size=32,"

### Pattern 2: Echo-Back-and-Command
He selects a phrase from the AI's previous response, pastes it, and adds a directive like "do this" or "do these."

> `rollout-2026-01-28T15-57-48`: "Leave host selection to your shell (hosted vs self-hosted) so your charts open at the correct site\n\njust do this"

> `rollout-2026-01-31T06-25-34`: "Precompute series tensors once, Replace batch topk merge:, Use torch.inference_mode() in score_user - do these"

> `rollout-2026-01-31T06-30-50`: "which is the biggest win? do that"

### Pattern 3: Rapid Session Cycling
He opens sessions, types (or submits the auto-populated context), and immediately abandons — sometimes 3 times in a row before settling. This happened across multiple clusters:

> Sessions 3→4→5 (Jan 28, 17:07-17:08): Same "how to improve my model?" question, abandoned at 10s, 0.1s, then kept at 237s.

> Sessions 8→9 (Jan 29, 10:18): Same "test loss not decreasing" prompt, abandoned at 0.2s, then kept at 1226s.

> Sessions 12→13 (Jan 29, 13:21-13:23): Same recommendation feature request, abandoned at 7s, then kept at 1478s.

> Sessions 26→27→28 (Jan 31, 06:14-06:30): Same "can recommend.py be made faster?" question, abandoned at 512s, kept at 22s (answer only), then kept at 193s (implementation).

### Pattern 4: Monosyllabic Continuations
When the AI proposes something and asks for confirmation, Nikhil replies with single words.

> `rollout-2026-01-31T06-30-50`: "yes"

> `rollout-2026-01-31T06-49-43`: "yes"

> `rollout-2026-01-30T16-47-16`: "yes do it anywhere necesssary. what baout floats.npz"

> `rollout-2026-01-31T06-30-50`: "do what you recommend"

### Pattern 5: GPU Memory as Status Update
On Jan 31, he starts providing nvidia-smi memory readings as raw data for the AI to use in optimization decisions.

> `rollout-2026-01-31T06-30-50`: "2479MiB / 15360MiB\nnvidia-smi"

> `rollout-2026-01-31T06-30-50`: "3609MiB / 15360MiB"

> `rollout-2026-01-31T06-30-50`: "Generating recommendations:   0%|                    | 1024/1362277 [00:24<9:06:17, 41.53it/s]\n 3611MiB / 15360MiB"

## Blind Spots

**He never provides expected vs. actual behavior.** His prompts show what happened but almost never state what he expected to happen. Error pastes show the error; they don't say what success looks like.

> `rollout-2026-01-28T15-57-48`: "link doesnt open" — but doesn't say which link or where he expected it to open.

> `rollout-2026-01-29T10-18-05`: "test loss not decreasing" — but doesn't state what test loss value would be acceptable.

**He never specifies constraints or preferences upfront.** Architecture decisions, performance targets, or quality requirements only emerge mid-session after the AI proposes something.

> `rollout-2026-01-29T13-23-37` (3rd message, not 1st): "col/src/driver.py is my driver file, recommendation should be triggered from this file only not separately"

> `rollout-2026-01-31T06-55-05` (1st message): "currently recommend.py takes 8:47:41 time to run, i want to run in 3 hrs" — this is his only session that gives a quantitative target in the first message.

**He never provides examples of desired output.** When asking for new features (recommendations, data formats), he describes the shape abstractly but never shows a concrete example of what a result should look like.

**He never answers the AI's clarifying questions directly.** When the AI asks "hosted vs self-hosted?", he responds with more terminal output instead.

> `rollout-2026-01-28T15-57-48`: AI asks "Do you intend to use hosted W&B or your self-hosted server?" → Nikhil responds with env var code block, not an answer.

**He never mentions previous sessions.** Each session starts from scratch — he never says "continuing from where we left off" or references what the AI did in a prior session.

**He never requests specific output formats.** He doesn't ask for code with comments, step-by-step explanations, or comparisons. The AI decides the response format every time.

## Raw Data

| Session | Date | First Message (truncated) | Length | Style |
|---------|------|--------------------------|--------|-------|
| 01-28T15-51 | Jan 28 | pip install wandb... error output | Long | Terminal paste |
| 01-28T15-57 | Jan 28 | wandb login --relogin... link doesnt open | Long | Terminal paste + complaint |
| 01-28T17-07 | Jan 28 | python driver.py... how to improve my model? | Long | Training logs + vague question |
| 01-28T17-08:14 | Jan 28 | python driver.py... how to improve my model? | Long | Same as above (duplicate) |
| 01-28T17-08:38 | Jan 28 | python driver.py... how to improve my model? | Long | Same as above (3rd attempt) |
| 01-28T20-00 | Jan 28 | how can i improve my model? | Short | Vague question |
| 01-28T21-21 | Jan 28 | i changed NEG_PER_POS from 7 to 20... | Short | Result report |
| 01-29T10-18:05 | Jan 29 | python driver.py... test loss not decreasing | Long | Training logs + observation |
| 01-29T10-18:14 | Jan 29 | python driver.py... test loss not decreasing | Long | Same (retry) |
| 01-29T11-47 | Jan 29 | python driver.py... why is my test loss not decreasing? | Long | Training logs + question |
| 01-29T12-06 | Jan 29 | hi | Minimal | Throwaway |
| 01-29T13-21 | Jan 29 | i want to now generate recommendations... | Medium | Feature request |
| 01-29T13-23 | Jan 29 | i want to now generate recommendations... | Medium | Feature request (retry) |
| 01-29T20-07 | Jan 29 | is my [code block]... | Medium | Code review |
| 01-29T20-30 | Jan 29 | can you fix error in model.py | Short | Fix command |
| 01-29T20-37 | Jan 29 | can you fix error in training.py | Short | Fix command |
| 01-29T20-44 | Jan 29 | will my test_loader generate same... | Medium | Technical question |
| 01-29T20-53 | Jan 29 | fix my recommend.py | Short | Fix command |
| 01-29T22-46 | Jan 29 | python driver.py... [training output] | Long | Terminal paste |
| 01-29T22-48 | Jan 29 | this is my main file... look at all the files | Medium | Code review request |
| 01-29T23-07 | Jan 29 | python driver.py... [training output] | Long | Terminal paste |
| 01-29T23-17 | Jan 29 | if i use MODE='prod'... | Short | Config question |
| 01-30T13-43 | Jan 30 | exhausting permanent memory... just tell dont edit code | Medium | Problem + constraint |
| 01-30T16-43 | Jan 30 | python driver.py... Traceback | Long | Error paste |
| 01-30T16-47 | Jan 30 | python driver.py... Traceback | Long | Error paste (retry) |
| 01-31T06-14 | Jan 31 | can recommend.py be made faster? how? | Short | Performance question |
| 01-31T06-23 | Jan 31 | can recommend.py be made faster? how? | Short | Same (retry) |
| 01-31T06-25 | Jan 31 | can recommend.py be made faster? how? | Short | Same (3rd attempt) |
| 01-31T06-30 | Jan 31 | can recommend.py be made faster? how? | Short | Same (4th attempt) |
| 01-31T06-49 | Jan 31 | how to make my recommend.py faster? | Short | Slight rephrasing |
| 01-31T06-55 | Jan 31 | recommend.py takes 8:47:41, want 3 hrs | Medium | Quantitative target |
| 01-31T07-51 | Jan 31 | this is from another repo [code paste] | Long | Cross-repo code reference |
| 01-31T07-59 | Jan 31 | remove series already read by user... | Medium | Feature request |
| 01-31T08-17 | Jan 31 | break recommend.py into functions... | Medium | Refactoring + constraints |
