Batch Prompting
Why send ten separate prompts when one can do the work of all ten? Batch Prompting groups multiple task instances into a single prompt — reducing API costs, cutting latency, and maintaining comparable accuracy by processing everything in one efficient pass.
Introduced: Batch Prompting was formalized in 2023 by Cheng et al. as a practical efficiency optimization for LLM workloads. The core idea is simple but powerful: instead of making separate API calls for each task instance, group multiple inputs into a single prompt and instruct the model to generate answers for all of them at once. The research showed this approach reduces token costs proportionally to batch size while maintaining accuracy within 1-2% of individual prompting across most tasks.
Modern LLM Status: Batch Prompting remains highly practical in 2026. With context windows expanding to 200K+ tokens, batching multiple tasks is a standard cost-optimization strategy for production systems. Most major API providers now support native batching endpoints that build on this technique’s principles. The approach has become essential infrastructure for anyone running LLM workloads at scale, from data labeling pipelines to content generation workflows.
One Prompt, Many Answers
Every API call to a language model carries overhead: network latency, prompt parsing, system prompt processing, and per-request costs. When you have dozens or hundreds of similar tasks — classifying emails, translating sentences, extracting data from records — sending each one individually is like mailing letters one at a time when you could put them all in one envelope.
Batch Prompting eliminates this redundancy. You provide a single set of instructions followed by all your inputs, numbered or labeled, and ask the model to process each one. The instructions are parsed once, the model maintains consistent interpretation across all items, and you get all results back in a single response. The cost savings scale linearly with batch size.
Think of it like a teacher grading papers. Reading the rubric once and grading 30 essays in sequence is far more efficient than re-reading the rubric before each individual essay.
A common concern is that processing multiple items at once might reduce quality. In practice, the opposite often happens: the model benefits from seeing related examples together, which creates implicit few-shot context. Processing “classify these 10 emails” gives the model a richer understanding of the classification space than processing each email in isolation. The key constraint is context window size — you must ensure all items plus the instructions fit within the model’s token limit.
The Batch Prompting Process
Four stages from individual tasks to efficient batch processing
Define the Task Instructions Once
Write clear, complete instructions for the task type. These instructions will be shared across all items in the batch, so they need to be general enough to cover every input but specific enough to produce consistent results. Include output format requirements so responses are easy to parse programmatically.
“Classify each of the following customer support messages into one of these categories: Billing, Technical, Account, Shipping, or General. For each message, respond with only the message number and category.”
Group and Number Your Inputs
Collect all task instances and present them with clear numbering or labeling. Consistent formatting helps the model track which response corresponds to which input. For best results, keep items within a batch at similar complexity levels — mixing trivial and complex items can cause the model to rush through some or over-analyze others.
1. “I was charged twice for my last order”
2. “The app crashes when I try to upload photos”
3. “How do I change my password?”
4. “My package hasn’t arrived in 2 weeks”
5. “Do you have a student discount?”
Model Processes All Items in One Pass
The model reads the instructions once and applies them to every numbered item sequentially. Each response inherits the same interpretation of the instructions, ensuring consistency across the batch. The model effectively amortizes the instruction-understanding cost across all items rather than re-interpreting instructions for each call.
1. Billing
2. Technical
3. Account
4. Shipping
5. General
Parse and Distribute Results
Extract individual results from the batched response and map them back to original inputs. Well-defined output formats (numbered lists, JSON, CSV) make parsing straightforward. Always include validation to catch cases where the model might skip an item or merge two responses — batch processing rarely fails completely, but individual items can occasionally be mishandled.
Parse the numbered results into a dictionary: {1: “Billing”, 2: “Technical”, 3: “Account”, 4: “Shipping”, 5: “General”}. Verify count matches input count. Route each ticket to the appropriate support queue.
See the Difference
Why batching delivers the same results at a fraction of the cost
Individual Prompting
Call 1: “Summarize this article in one sentence: [Article A]”
Call 2: “Summarize this article in one sentence: [Article B]”
Call 3: “Summarize this article in one sentence: [Article C]”
Call 4: “Summarize this article in one sentence: [Article D]”
Call 5: “Summarize this article in one sentence: [Article E]”
5 API calls. Instructions parsed 5 times. 5x network roundtrips. Each call pays full per-request overhead. Total latency is cumulative if sequential, or requires parallel infrastructure.
Batch Prompting
Instructions: “Summarize each of the following 5 articles in one sentence each. Number your responses to match the article numbers. Note: Always verify AI-generated summaries against the original articles for accuracy.”
1. [Article A]
2. [Article B]
3. [Article C]
4. [Article D]
5. [Article E]
1 API call. Instructions parsed once. 1 network roundtrip. Minimal per-request overhead. All 5 summaries returned in a single response with consistent formatting.
Practice Responsible AI
Always verify AI-generated content before use. AI systems can produce confident but incorrect responses. When using AI professionally, transparent disclosure is both best practice and increasingly a legal requirement.
48 US states now require AI transparency in key areas. Critical thinking remains your strongest tool against misinformation.
Batch Prompting in Action
See how batching scales efficiency across different task types
“Analyze the sentiment of each customer review below. For each, provide: the review number, sentiment (Positive/Negative/Mixed), and a one-phrase reason. Always remember that AI sentiment analysis should be verified by humans before taking action on individual customer cases.
1. ‘The product arrived fast and works perfectly. Love it!’
2. ‘Terrible quality. Broke after one week of normal use.’
3. ‘Good features but the battery life is disappointing.’
4. ‘Customer service was amazing when I had an issue.’
5. ‘Not worth the price. Cheaper alternatives do the same thing.’
6. ‘Been using it daily for 6 months with zero problems.’”
1. Positive — Satisfied with speed and functionality
2. Negative — Product durability failure
3. Mixed — Feature praise tempered by battery complaint
4. Positive — Service recovery appreciated
5. Negative — Poor perceived value versus alternatives
6. Positive — Long-term reliability confirmed
“Extract the company name, funding amount, and funding round from each news snippet below. Return results as numbered entries. If any field is unclear, mark it as ‘Not specified.’ Verify all extracted data against original sources before using in reports.
1. ‘Acme Robotics announced today it has raised $45 million in its Series B round led by Venture Partners.’
2. ‘The AI startup NeuralPath secured seed funding of $8M to expand its research team.’
3. ‘CloudScale Technologies closed a $120M growth round, bringing total funding to over $200M.’
4. ‘HealthBridge received an undisclosed investment from major healthcare investors to accelerate clinical trials.’”
1. Company: Acme Robotics | Amount: $45M | Round: Series B
2. Company: NeuralPath | Amount: $8M | Round: Seed
3. Company: CloudScale Technologies | Amount: $120M | Round: Growth
4. Company: HealthBridge | Amount: Not specified | Round: Not specified
“Translate each of the following English phrases into Spanish and French. Number your responses to match. Note: AI translations should be reviewed by a native speaker before use in official communications.
1. ‘Your order has been shipped and will arrive within 3-5 business days.’
2. ‘We apologize for the inconvenience. A full refund has been processed.’
3. ‘Thank you for your feedback. We are working to improve this feature.’
4. ‘Your subscription will renew automatically on the first of next month.’”
1.
Spanish: Su pedido ha sido enviado y llegará en un plazo de 3 a 5 días hábiles.
French: Votre commande a été expédiée et arrivera dans un délai de 3 à 5 jours ouvrables.
2.
Spanish: Lamentamos las molestias. Se ha procesado un reembolso completo.
French: Nous nous excusons pour le désagrément. Un remboursement intégral a été effectué.
3.
Spanish: Gracias por sus comentarios. Estamos trabajando para mejorar esta función.
French: Merci pour vos commentaires. Nous travaillons à améliorer cette fonctionnalité.
4.
Spanish: Su suscripción se renovará automáticamente el primer día del próximo mes.
French: Votre abonnement sera renouvelé automatiquement le premier du mois prochain.
When to Use Batch Prompting
Best for processing multiple similar tasks efficiently at scale
Perfect For
Classifying hundreds of support tickets, categorizing products, or labeling data points — batch the items and get all labels in one call.
Pulling structured information from unstructured text at scale — names, dates, amounts, categories from documents, emails, or records.
Generating product descriptions, meta tags, social media posts, or email subject lines — define the template once, generate for all items in a batch.
Any scenario where LLM API costs are a significant line item — batching can reduce costs by 60-80% depending on batch size and task complexity.
Skip It When
Complex analysis, long-form writing, or tasks where each item needs its own tailored approach — batching these risks shallow treatment of each item.
When total input size (instructions + all items) exceeds the model’s context window — split into smaller batches rather than forcing everything into one call.
When each user query needs an immediate response — batching introduces wait time while accumulating items. Better for async processing pipelines than live chat.
Use Cases
Where Batch Prompting delivers the most value
E-Commerce Catalogs
Generate product descriptions, extract attributes, or classify items across entire catalogs. Batch 50-100 products per prompt to generate consistent, formatted descriptions in minutes instead of hours.
Document Processing
Extract key fields from invoices, contracts, or forms. Batch multiple documents in a single prompt to build structured data from unstructured sources at scale.
Content Moderation
Screen user-generated content for policy violations at volume. Batch flagged items for review, classify severity levels, and generate moderation notes — all in a single prompt call.
Localization Pipelines
Translate UI strings, marketing copy, or documentation across multiple languages simultaneously. Batch all strings in one prompt per target language for consistent terminology.
Security Log Analysis
Classify batches of security alerts, extract indicators of compromise from log entries, and triage incidents by severity — processing hundreds of entries in a single prompt.
Survey Analysis
Process open-ended survey responses at scale: extract themes, classify sentiment, and identify key quotes. Batch 20-50 responses per prompt for efficient qualitative analysis.
Where Batch Prompting Fits
Batch Prompting optimizes throughput in the efficiency dimension
The ideal batch size depends on task complexity and model context limits. For simple classification tasks, batches of 50-100 items work well. For tasks requiring more nuanced output (summaries, translations), 10-20 items per batch maintains quality. Start with small batches, compare accuracy against individual prompting, and scale up once you have confirmed comparable quality. Always validate a sample of batch results against individually-processed items.
Related Techniques
Explore complementary efficiency techniques
Scale Your AI Workflows
Try Batch Prompting to process multiple tasks efficiently or explore our tools to optimize your prompt engineering workflow.