Most people discover AI batch processing through one use case — usually product descriptions or meta tags — and then realise the same approach applies to almost any task where you have a dataset and a consistent job to do for every row.
Here are ten tasks that work surprisingly well, including what to put in your CSV for each one.
SEO meta tags
Generate title tags and meta descriptions for every page on your site — with consistent keyword targeting and brand name formatting. The most common starting point for most users.
CSV columns: page_url, h1, page_type, top_keyword, content_summary. See the full how-to guide.
Product descriptions
Write unique, on-brand product descriptions for your full catalogue. Even a basic product data sheet is enough input — the model generates copy that reads as written for each specific product.
CSV columns: product_name, category, key_attributes, brand_voice_notes, target_customer. See the full how-to guide.
Google Ads copy
Generate RSA headlines and descriptions for every ad group in a campaign. Character limits are tight and format rules are strict — exactly the kind of constrained task where batch processing excels.
CSV columns: ad_group, keyword_theme, product_category, key_usp, intent. See the full case study.
Customer review analysis
Classify hundreds or thousands of reviews by theme, sentiment, and whether they need a response. Turn a raw export from Trustpilot, Google, or Amazon into a structured, filterable dataset.
CSV columns: review_id, review_text, rating, product_name. See the full how-to guide.
Email subject line personalisation
Generate personalised subject lines (or even opening paragraphs) for a segmented list. You provide the segment data — industry, job title, last purchase, stage in funnel — the model tailors each one.
CSV columns: first_name, company, industry, email_purpose, recent_action.
Lead enrichment
Add context to a CRM export: ICP fit scores, messaging angles, company summaries, or personalised opening lines based on the company data you already have. Saves hours of manual research before outreach.
CSV columns: company_name, industry, company_size, known_pain_points, your_product_fit.
Survey and qualitative data coding
Classify open-ended survey responses, support tickets, or customer interviews against a defined taxonomy. What would take a team of coders two weeks can run overnight as a batch job.
CSV columns: response_id, response_text, respondent_segment. See the full case study.
Job description writing
Generate consistent, well-structured job descriptions from a hiring brief. Particularly useful for HR teams or recruiters building multiple roles simultaneously — each description is tailored to the role while staying consistent with company voice.
CSV columns: job_title, department, seniority, key_responsibilities, required_skills, company_values.
Content localisation
Adapt existing copy for different markets, audiences, or regional variations. Not just translation — tone adjustments, local references, terminology changes — applied consistently across a full content set.
CSV columns: content_id, original_copy, target_market, localisation_notes, tone_adjustments.
Document summarisation
Summarise a set of documents — reports, articles, proposals, transcripts — in a consistent format. Useful for research teams, analysts, or anyone who needs to process a backlog of long-form content into something actionable.
CSV columns: document_id, document_title, full_text (or key_excerpts), desired_summary_format, max_length.
The pattern behind all of them
Every task on this list follows the same shape: a dataset where each row represents one item, a consistent job to do for each row, and a desired output format. If your task fits that shape, batch processing is likely a good fit.
The one variable that matters most is how well you define the task in your batch instructions. The model is capable — the ceiling is almost always the quality of the instructions, not the model's ability. Spend 15 minutes getting the instructions right and test on 20 rows before scaling to 2,000.