Data Team Playbook: GLM Analysis Task Template Pack

Most day-to-day work on a data team is structured and repetitive: writing SQL, reading numbers, producing reports, and doing attribution. The GLM-5/5.3 series' reasoning and coding strength in Chinese, combined with the thinking mode for deeper analysis, fits these tasks well. Below are three ready-to-paste templates designed for both the BigModel API and the web chat interface — just swap in your variables.

Template 1: SQL Generation and Debugging

The most time-consuming part of analytics is often not the analysis itself but translating business questions into correct SQL. GLM's coding capability covers common dialects (MySQL, Hive SQL, ClickHouse) reasonably well. The key is to feed the schema clearly and ask the model to restate its understanding before writing SQL — this noticeably reduces misinterpretation:

You are a senior data engineer. Generate SQL based on the following.

[Dialect] {{MySQL / Hive / ClickHouse}}
[Schema]
{{paste CREATE TABLE statements or a field list with names, types, comments}}
[Business question] {{e.g., daily active users over the last 30 days, excluding internal accounts}}
[Definitions]
- Active user definition: {{write it out if you have one}}
- Internal account flag: {{field and values}}

Requirements:
1. First restate your understanding of the request in 3 sentences, then write the SQL;
2. Add comments in the SQL;
3. Flag potential data pitfalls (time zones, dedup logic, NULL handling);
4. If information is missing, list the questions you need answered — do not guess fields.

Before running in production, have the model generate expected output for a few sample rows and compare against real results.

Template 2: Data Interpretation Report

For the interpretation step after data pulls, GLM's Chinese writing quality is a clear strength. The trick in this template is constraining the audience and length to keep the model from producing empty boilerplate:

You are a data analysis consultant. Write an interpretation report based on the data below.

[Data] {{paste the metric results table or a CSV snippet}}
[Context] {{business scenario, reporting period, prior-period baseline}}
[Audience] business stakeholders, non-technical
[Length] under 500 words

Output structure:
1. One-sentence conclusion (lead with the most important point);
2. 2–3 key findings, each backed by data;
3. Anomaly notes: which numbers deviate from baseline, plausible causes (separate "supported by data" from "needs further validation");
4. 1–2 recommended next steps.

Forbidden: vague summaries, generic advice unrelated to the data, or inventing numbers not present in the data.

Template 3: Metric Anomaly Attribution

Attribution is reasoning-heavy. Enable the thinking mode so the model works through the analysis before concluding — output quality is more consistent than asking for a direct answer:

You are a data analysis expert. Perform attribution analysis on the following anomaly.

[Anomaly] {{e.g., DAU down 12% week-over-week; historical range ±3%}}
[Available data]
- Dimension breakdown: {{channel / region / platform / new-vs-returning data}}
- Related events: {{releases, campaign start/end, channel changes}}
[Requirements]
1. List all plausible causes first, grouped into "data-side / product-side / external";
2. Validate each against the dimension breakdown, quantifying contribution;
3. State the primary cause with a confidence level, and where remaining uncertainty lies;
4. Specify what additional data is needed to confirm further.

Engineering Tips

Turning these templates into team assets beats one-off use. First, wrap the templates into internal services or scripts via the BigModel API, parameterize the variable slots, and let teammates call them as needed instead of everyone writing their own prompts. Second, keep schemas and metric definitions as standing documents injected alongside the template, cutting repeated pasting. Third, for on-premise scenarios, the open-weight GLM models can be deployed inside your network so sensitive data stays in-domain — deployment details are subject to the official documentation.

Compared with GPT and Claude, GLM tends to feel more natural in Chinese business contexts and with domestic stacks like Hive or MaxCompute; Kimi and DeepSeek occupy a similar space, so run the same templates against your real data on each before picking a primary model — your own benchmarks beat any review article.

The core idea of this pack: bake "restate-and-confirm, definitions up front, quantified validation" into the prompts, so the model produces an auditable analysis process rather than a bare number. Adapt these into your team's standard SOP — the templates aren't version-locked and work from the GLM-4 series through the 5 series, with the main difference being reasoning depth in thinking mode.