Many-Shot Prompting
When a handful of examples falls short, fill the context window. Many-Shot Prompting provides the model with hundreds or thousands of demonstrations — leveraging massive context windows to teach complex patterns through sheer volume of evidence rather than clever instruction.
Introduced: Many-Shot In-Context Learning was formalized in 2024 by Agarwal et al. at Google DeepMind. The technique capitalizes on a simple observation: as LLM context windows expanded from 4K to 100K+ tokens, the old practical limit of a few examples disappeared entirely. The researchers demonstrated that providing hundreds to thousands of examples in-context could significantly outperform few-shot approaches, particularly for tasks requiring the model to internalize complex patterns, specialized formats, or nuanced classification boundaries that a handful of examples cannot adequately convey.
Modern LLM Status: Many-Shot ICL is an active and increasingly important technique. With modern LLMs supporting context windows of 100K+ tokens (Claude, Gemini), many-shot prompting has become more practical and effective than ever. Research from Google DeepMind (2024) demonstrated that using hundreds to thousands of examples in-context can significantly outperform few-shot approaches, especially for tasks where the model needs to learn complex patterns, specialized formats, or domain-specific reasoning that cannot be conveyed with just a handful of examples. The technique is especially relevant for tasks requiring consistent formatting, nuanced classification, or adaptation to specialized domains.
More Examples, Better Patterns
Few-shot prompting works well for straightforward tasks, but it has a fundamental limitation: a small number of examples can only represent a narrow slice of the problem space. When your task involves 25 categories instead of 3, or when edge cases are common rather than rare, a handful of demonstrations leaves the model guessing about everything it has not seen. Many-Shot Prompting eliminates that guesswork by flooding the context with evidence.
The approach is deliberately simple. Rather than engineering clever instructions or multi-step reasoning chains, you let the examples do the teaching. Hundreds of input-output pairs establish the pattern so unambiguously that the model absorbs formatting rules, classification boundaries, and domain vocabulary purely from observation — the same way a human might learn a new coding style by reading hundreds of examples rather than studying a style guide.
Think of it as the difference between explaining the rules of a board game verbally versus simply playing fifty rounds. After enough rounds, the rules become intuitive — even the subtle ones nobody thought to mention.
Few-shot examples inevitably create selection bias — whichever 3-5 examples you choose will overrepresent some patterns and miss others entirely. Many-Shot sidesteps this by including enough examples to cover the full distribution of inputs. The model sees common cases, rare edge cases, ambiguous borderlines, and formatting exceptions all within the same prompt. This statistical coverage produces more consistent and reliable outputs than any carefully curated small set can achieve.
The Many-Shot Process
Four stages from example collection to pattern-driven output
Assemble a Large Example Set
Gather dozens to thousands of input-output examples that represent the full scope of your target task. Prioritize coverage over perfection — include common cases, edge cases, ambiguous inputs, and the complete range of expected output categories. The goal is to build a dataset that mirrors the real-world distribution the model will encounter.
For a support ticket classifier with 25 categories, collect 10-20 labeled tickets per category — yielding 250-500 examples that cover every routing destination including rare ones like “billing dispute escalation” or “API rate limit inquiry.”
Format Examples Consistently
Structure every example with identical formatting — the same delimiters, labels, and input-output markers throughout the entire set. Consistency in presentation is what allows the model to distinguish the pattern signal from noise. Even minor formatting inconsistencies across hundreds of examples can degrade performance because the model wastes capacity learning format variations instead of task patterns.
Every example follows the same template: Input: [customer message] / Category: [label] / Priority: [high|medium|low]. No deviations, no extra fields in some examples, no missing labels in others.
Pack Examples into Context
Include as many examples as the context window allows, prioritizing diversity and coverage over redundancy. If you cannot fit all examples, select a representative subset that maintains proportional coverage of each category or pattern. Order can matter — place the most representative examples early and ensure that no category or pattern type is clustered exclusively at the end where it might receive less attention.
With a 128K-token context window and examples averaging 80 tokens each, you can fit roughly 1,200 examples with room for instructions and the target query. Interleave categories rather than grouping them to ensure even exposure.
Present the Target Query
After all demonstrations, present the new input using the exact same format as the examples — minus the output. The model completes the pattern by generating an output that follows the conventions established across hundreds of prior demonstrations. The sheer weight of consistent examples makes deviation from the learned pattern extremely unlikely.
Input: “My API calls are returning 429 errors since yesterday and I need this resolved for a demo tomorrow morning.” / Category: [model generates: API rate limit inquiry] / Priority: [model generates: high]
See the Difference
Why hundreds of examples outperform a handful
Few-Shot (3 Examples)
Classify the sentiment of product reviews. Here are 3 examples:
“Love this product!” → Positive
“Terrible quality.” → Negative
“It works fine.” → Neutral
Now classify: “The build quality is outstanding but the software crashes every hour and customer support ghosted me.”
Negative.
Many-Shot (200+ Examples)
Examples covering: positive, negative, neutral, mixed-positive, mixed-negative, sarcastic, conditional-positive, feature-specific praise with overall dissatisfaction, and dozens more nuanced patterns — each with consistent labeling across the full taxonomy.
Mixed-Negative. Hardware praised (build quality: positive), software criticized (crashes: negative), service criticized (support: negative). Overall lean: negative due to two negative dimensions outweighing one positive.
Natural Language Works Too
While structured frameworks and contextual labels are powerful tools, LLMs are exceptionally good at understanding natural language. As long as your prompt contains the actual contextual information needed to create, answer, or deliver the response you’re looking for — the who, what, why, and constraints — the AI can produce complete and accurate results whether you use a formal framework or plain conversational language. But even in 2026, with the best prompts, verifying AI output is always a necessary step.
Many-Shot in Action
See how large example sets transform model performance
A customer support team needs to route incoming tickets into 25+ categories with subcategories. Few-shot attempts with 3-5 examples per category fail on ambiguous tickets — a complaint about “slow API responses during peak hours” gets routed to “General Performance” instead of “API Rate Limiting — Infrastructure.”
Example set: 200 labeled tickets covering all 25 categories, including 15-20 deliberately ambiguous cases that demonstrate correct routing for borderline inputs.
Key examples included:
“Dashboard loads slowly after 3pm EST” → Infrastructure — Peak Load
“API returns 429 too many requests” → API Rate Limiting — Infrastructure
“Everything feels sluggish today” → General Performance — Triage Needed
“API response times doubled since last deployment” → API Performance — Deployment Related
Result on ambiguous ticket: “Slow API responses during peak hours” correctly routed to “API Rate Limiting — Infrastructure” because the model absorbed the distinction between general slowness and API-specific performance from dozens of similar boundary cases.
A pharmaceutical company needs to translate clinical trial documentation from English to Japanese, maintaining exact medical terminology. Few-shot with 5 term pairs produces inconsistent translations — “adverse event” alternates between three different Japanese renderings across a single document.
Example set: 500 verified English-Japanese medical term pairs from the company’s approved glossary, plus 150 full-sentence translation pairs from previously approved clinical documents.
Coverage includes: Drug names, anatomical terms, adverse event classifications, regulatory phrases, statistical terminology, and dosage expressions — each appearing in multiple sentence contexts to reinforce correct usage.
Result: Translations maintain terminological consistency across the entire document. “Adverse event” renders identically every time because the model saw the approved translation in 40+ different sentence contexts within the prompt. Domain-specific compounds like “double-blind placebo-controlled” translate correctly because the model observed the exact pattern in its demonstrations rather than attempting creative translation.
A data migration project requires converting addresses from 12 different legacy formats into a single standardized schema. Few-shot with one example per format misses regional variations — UK postcodes, German PLZ codes, and Japanese address ordering each have dozens of sub-formats that a single example cannot represent.
Example set: 800 address conversion pairs spanning all 12 source formats, with 30-100 examples per format weighted by frequency and variation complexity.
Format coverage:
UK: “Flat 3B, 42 High St, London SW1A 1AA” → standardized
Germany: “Hauptstraße 15, 80331 München” → standardized
Japan: “150-0001 Tokyo Shibuya-ku...” → standardized
US (variations): PO Box, rural routes, military APO, suite/unit formats — 80+ examples covering the long tail of American address formatting.
Result: The model handles novel addresses from any of the 12 formats with over 97% accuracy, including edge cases like addresses with building names, floor numbers, care-of lines, and mixed-language entries — because it absorbed the transformation rules from hundreds of real-world examples rather than relying on explicit format specifications.
When to Use Many-Shot
Best when examples teach better than instructions
Perfect For
Tasks involving dozens of output categories where boundaries are subtle — few-shot cannot represent enough of the decision space to produce consistent results.
Work requiring specialized vocabulary, formatting conventions, or reasoning patterns that the model has not internalized from general training — medical coding, legal terminology, proprietary taxonomies.
Converting diverse input formats to a single standard where the transformation rules are easier to demonstrate than to describe — addresses, dates, names, product codes.
When few-shot results vary depending on which examples you choose, it signals that the task needs broader coverage — many-shot eliminates selection sensitivity.
Skip It When
If 3-5 examples already produce consistent, accurate results, scaling to hundreds adds cost and latency without measurable improvement — do not waste tokens on solved problems.
Many-shot amplifies the quality of your examples. If your labeled data contains errors, inconsistencies, or outdated patterns, scaling up will teach the model bad habits at scale rather than good ones.
Models with context windows under 32K tokens cannot fit enough examples for many-shot to outperform well-chosen few-shot examples — the technique requires room to scale.
Use Cases
Where Many-Shot delivers the most value
Data Labeling
Annotate large datasets by providing hundreds of labeled examples in-context, achieving near-human accuracy on classification, entity extraction, and tagging tasks without fine-tuning.
Format Normalization
Standardize inconsistent data entries — dates, phone numbers, addresses, product identifiers — by demonstrating the transformation with hundreds of real-world conversion pairs.
Code Style Enforcement
Teach a model your team’s exact coding conventions by providing hundreds of before-and-after code samples, ensuring generated code matches your naming patterns, spacing rules, and documentation standards.
Medical Coding
Map clinical notes to ICD-10 or CPT codes using hundreds of verified coding examples, capturing the nuanced distinctions between similar diagnoses that few-shot approaches routinely confuse.
Legal Clause Classification
Categorize contract clauses into regulatory categories by demonstrating hundreds of clause-to-category mappings, enabling consistent treatment of indemnification, limitation of liability, force majeure, and other legal constructs.
Multilingual Content Standardization
Normalize content across languages and locales by providing translation pairs, terminology mappings, and formatting conversions that maintain brand voice and technical accuracy across every target market.
Where Many-Shot Fits
Many-Shot occupies the high-volume zone of in-context learning
Many-Shot becomes even more powerful when combined with Reinforced In-Context Learning. Instead of packing examples randomly, you can use the initial model outputs to identify which example categories produce the most errors, then over-represent those categories in your final example set. This targeted approach means your context window budget is spent on the examples that matter most — the boundary cases and ambiguous inputs where additional demonstrations make the biggest difference.
Related Techniques
Explore complementary in-context learning techniques
Scale Your Examples
Try many-shot prompting with hundreds of examples or build prompts with our tools.