Batch Text Analysis with an LLM CLI: 500 Reviews, One For Loop, a Few Cents

I tried to get an AI to label 500 Etsy reviews by pasting them into a chat tab, twenty at a time. By batch three, the model had quietly invented a new category that wasn't in my labeling scheme. That's not a model problem — it's a container problem. Long conversations drift; batch work needs rules that don't.

So I moved the whole job into the terminal. This post is the full walkthrough: sentiment labeling for 500 reviews, a contract risk checklist, an AI-writing check for a thesis chapter, and turning a 2-hour lecture video into Obsidian notes. Total cost for the week: under $0.50.

The Tool

bl is the CLI for Alibaba Cloud Model Studio. Install with Node.js:

npm install -g bailian-cli

Get a free API key from the Model Studio console (new accounts include a free tier), then:

bl auth login

Pattern: Rules as Files, Input as Files, Output as JSON

The whole approach fits in one sentence: put your labeling rules in a file, feed inputs from files, write structured output to files. Every call gets identical rules — drift is gone by construction.

rule.txt:

You are a review analyzer. For the input review output JSON: {"sentiment": "positive/negative/neutral", "aspect": [matching items from "quality","shipping","packaging","price","service"], "intent": "praise/complaint/suggestion/question", "summary": "under 10 words"}. Output JSON only.

The loop:

for f in reviews/*.txt; do bl text chat --model qwen-turbo --system "$(cat rule.txt)" --message "$(cat "$f")" --output json > "out/$(basename "$f" .txt).json"; done

Three deliberate choices:

• --model qwen-turbo: labeling is factory work, not reasoning work. On a 50-sample side-by-side, the budget tier matched the flagship default — at an order of magnitude lower cost. The 500-review batch cost cents.
• --output json: results feed straight into a spreadsheet or script. (Piped output defaults to JSON anyway.)
• File-per-review: 3 items failed on special characters; retrying meant re-running exactly 3 files, not the batch.

Sequential run: ~40 minutes. With --concurrent 4: ~11 minutes.

The aggregate: 62% of negative sentiment pointed at "shipping," and 17 product suggestions were hiding inside 5-star reviews. My friend had skimmed those reviews for three evenings and come away with vibes; the loop came back with numbers.

Contract → Risk Checklist

Same command, different contract (pun intended). The trick is the negative constraint:

bl text chat --system "You are a contract reviewer. List ONLY clauses that are unfavorable or unusual for the contractor. For each: quote the exact wording, explain the risk, suggest a redline. Skip boilerplate." --message "$(cat agreement.txt)"

Without "skip boilerplate," the model dutifully reviews every standard clause and buries the signal. With it, my 9-page freelance agreement came back as four flagged items — including an IP-assignment clause that kicked in before final payment. Each with exact quotes I could paste into my reply.

Boundary: this is triage. For real money, the checklist is what you bring to a lawyer.

AI-ness Check for a Thesis

Universities now run AI-content detection on submissions, and my cousin needed a self-check. Honest framing first: no tool guarantees passing any detector, and AI-detecting-AI has real false positives. What works is humbler — locate the most machine-flavored paragraphs and get sentence-level rewrite directions:

bl text chat --system "Analyze the input for AI-generation markers: mechanical parallel structures, stock transitions, vague claims with no concrete detail. Rate each paragraph High/Medium/Low and give specific humanization suggestions, down to the sentence." --message "$(cat chapter3.txt)"

Two paragraphs came back High — both were AI output she'd barely edited. She rewrote them herself using the suggestions.

2-Hour Lecture → Obsidian Notes

Different command, same philosophy. bl vision describe takes a local video file directly:

bl vision describe --video ./lecture.mp4 --prompt "This is a course lecture. Output Markdown study notes: chapters in teaching order, each with core arguments, key examples, and any formulas or code. End with a takeaway list. Use Obsidian-compatible syntax."

Minutes later: chaptered notes. I watched 25 minutes of the video instead of 120. Dedicated summarizer tools are more polished (timestamps, browser extensions) but subscription-based; at two videos a month, pay-per-call wins and the note format is whatever my prompt says.

The Bill

bl usage stats --days 7 at the end of the week: everything above, under $0.50, mostly inside the free tier. bl usage free shows remaining free quota before you fire off a big batch — worth a glance.

When to Use This (and When Not)

Use it when the work is high-volume, repetitive, and needs structured output — reviews, tickets, surveys, contract triage, lecture backlogs. Skip it if you analyze one text a month (chat tab is fine) or need zero-error output (AI is the triage layer, not the signature).

If you've got a pile of unread text of your own, the free tier is enough to run everything in this post: sign up here, and the distance between "unread" and "read, with a checklist" is one for loop.

What's the biggest batch-text job you've been putting off? Tell me in the comments.