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In-Context Learning(ICL)

Conditioning a model on instructions and demonstrations in the current context, without updating model parameters

Complexity: mediumLearning and Adaptation

In 30 seconds

What: Conditions a model on task instructions and examples in the prompt, without changing model weights, to guide behavior for new inputs.
When to use: Tasks with clear, fixed categories or patterns where a few labeled examples reliably show the model what to do.
Watch out: Performance degrades sharply if examples don't match the distribution of real inputs or if the model misinterprets which features matter.

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Conditioning a model on instructions and demonstrations in the current context, without updating model parameters

Zero-shot instructions
Few-shot demonstrations
Example-based task specification
No parameter updates
Retrieval-based example selection
Prompt-order sensitivity testing

The Agent Architect

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References

The papers, specifications, and repositories this pattern is based on.

Language Models are Few-Shot Learners (2020)arXiv:2005.14165

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Patterns

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Design Patterns & Techniques

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🎯

In-Context Learning(ICL)

Conditioning a model on instructions and demonstrations in the current context, without updating model parameters

Complexity: mediumLearning and Adaptation

In 30 seconds

What: Conditions a model on task instructions and examples in the prompt, without changing model weights, to guide behavior for new inputs.
When to use: Tasks with clear, fixed categories or patterns where a few labeled examples reliably show the model what to do.
Watch out: Performance degrades sharply if examples don't match the distribution of real inputs or if the model misinterprets which features matter.

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30-Second Overview

Pattern: Provide task examples in prompt to enable model learning without parameter updates

Why: Enables rapid task adaptation, requires no training, leverages model's pattern recognition for immediate performance

Key Insight: Models can learn from demonstrations in context, performing implicit gradient descent during inference

Quick Implementation

1Select:Choose relevant examples for task context

2Format:Structure examples with input-output pairs

3Prompt:Combine examples with target query

4Infer:Model learns pattern from examples

5Generate:Apply learned pattern to new input

Example: examples + query → model_inference → output

Do's & Don'ts

✅Use diverse, high-quality examples representative of task

✅Maintain consistent formatting across all examples

✅Order examples from simple to complex when possible

✅Include edge cases and boundary conditions

✅Test with different example counts (1-shot to few-shot)

✅Use clear separators between examples

❌Use contradictory or inconsistent examples

❌Overload context with too many examples

❌Use biased or non-representative examples

❌Mix different task types in same prompt

❌Ignore example selection and ordering effects

When to Use

Use When

• Quick adaptation to new tasks needed
• Limited or no training data available
• Task requires demonstration over description
• Rapid prototyping and experimentation
• Model needs to understand complex patterns

Avoid When

• Large amounts of training data available
• Task requires extensive domain knowledge
• Context window limitations are severe
• High precision requirements exceed ICL capability
• Consistent performance across variations needed

Key Metrics

Few-Shot Accuracy

Performance with K examples

Example Efficiency

Performance gain per example

Context Utilization

Token usage vs performance

Task Transfer

Generalization to unseen inputs

Example Sensitivity

Performance variance across example sets

Ordering Robustness

Stability across example orders

Top Use Cases

Text Classification: Sentiment analysis, topic categorization with labeled examples

Data Extraction: Named entity recognition, information extraction from documents

Format Translation: JSON to XML, structured data transformation

Question Answering: Domain-specific QA with example question-answer pairs

Code Generation: Programming tasks with input-output code examples

Creative Writing: Style transfer, content generation with stylistic examples

References & Further Reading

Deepen your understanding with these curated resources

Foundational Papers

Language Models are Few-Shot Learners (Brown et al., 2020)

Learning to Retrieve Prompts for In-Context Learning (Rubin et al., 2021)

What Makes Good In-Context Examples for GPT-3? (Liu et al., 2021)

Calibrate Before Use: Improving Few-Shot Performance (Zhao et al., 2021)

Theoretical Understanding (2022-2024)

Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent (Dai et al., 2022)

Transformers Learn In-Context by Gradient Descent (von Oswald et al., 2022)

In-context Learning and Induction Heads (Olsson et al., 2022)

What learning algorithm is in-context learning? Investigations with linear models (Akyürek et al., 2022)

Example Selection & Optimization

Learning To Retrieve Prompts for In-Context Learning (Rubin et al., 2021)

In-Context Example Selection with Influences (Nguyen & Wong, 2023)

Active Example Selection for In-Context Learning (Zhang et al., 2022)

Compositional Exemplars for In-context Learning (Ye et al., 2023)

Chain-of-Thought & Reasoning

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models (Zhou et al., 2022)

Self-Consistency Improves Chain of Thought Reasoning in Language Models (Wang et al., 2022)

Large Language Models are Zero-Shot Reasoners (Kojima et al., 2022)

Recent Advances (2023-2024)

In-Context Learning Creates Task Vectors (Hendel et al., 2023)

Many-Shot In-Context Learning (Agarwal et al., 2024)

In-Context Learning with Long Context (Bertsch et al., 2024)

Understanding In-Context Learning via Supportive Pretraining Data (Shin et al., 2024)

Multimodal In-Context Learning

Flamingo: a Visual Language Model for Few-Shot Learning (Alayrac et al., 2022)

Multimodal Few-Shot Learning with Frozen Language Models (Tsimpoukelli et al., 2021)

GPT-4V(ision) System Card (OpenAI, 2023)

Visual In-Context Learning for Large Vision-Language Models (Yang et al., 2023)

Implementation Resources

LangChain Few-Shot Prompt Templates

OpenAI Best Practices for Prompt Engineering

Anthropic Prompt Engineering Guide

HuggingFace Transformers In-Context Learning

Datasets & Benchmarks

LAMA: LAnguage Model Analysis (Petroni et al., 2019)

BIG-bench: Beyond the Imitation Game Benchmark

HELM: Holistic Evaluation of Language Models

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding

Tools & Libraries

Guidance: A guidance language for controlling large language models

DSPy: Programming (not prompting) foundation models

PromptSource: An Integrated Development Environment for Natural Language Prompts

OpenPrompt: An Open-Source Framework for Prompt-learning

Analysis & Evaluation

Measuring and Narrowing the Compositionality Gap in Language Models (Press et al., 2022)

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? (Min et al., 2022)

Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations (Yoo et al., 2022)

Fantastically Ordered Prompts and Where to Find Them (Lu et al., 2021)

Community & Discussion

r/MachineLearning - In-Context Learning Discussions

AI Alignment Forum - In-Context Learning

EleutherAI Discord - Prompt Engineering Channel

Prompt Engineering Discord Community

Conditioning a model on instructions and demonstrations in the current context, without updating model parameters

Zero-shot instructions
Few-shot demonstrations
Example-based task specification
No parameter updates
Retrieval-based example selection
Prompt-order sensitivity testing

The Agent Architect

One pattern, one tradeoff, one production failure story. A short weekly briefing for people building agentic systems.

Weekly email, one-click unsubscribe. We only use your address to send the briefing.

References

The papers, specifications, and repositories this pattern is based on.

Language Models are Few-Shot Learners (2020)arXiv:2005.14165

From the engineer behind this catalog

Get your agent architecture reviewed

€750 instead of €1,500, one week, written report and walkthrough call, until 30 September

See the review

Loading patterns…

In-Context Learning(ICL)

In 30 seconds

Ask the AI expert about this pattern

In-Context Learning: Overview

The Agent Architect

References

Get your agent architecture reviewed

Patterns

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Design Patterns & Techniques

Loading patterns…

In-Context Learning(ICL)

In 30 seconds

Ask the AI expert about this pattern

30-Second Overview

Quick Implementation

Do's & Don'ts

When to Use

Use When

Avoid When

Key Metrics

Top Use Cases

References & Further Reading

Foundational Papers

Theoretical Understanding (2022-2024)

Example Selection & Optimization

Chain-of-Thought & Reasoning

Recent Advances (2023-2024)

Multimodal In-Context Learning

Implementation Resources

Datasets & Benchmarks

Tools & Libraries

Analysis & Evaluation

Community & Discussion

In-Context Learning: Overview

The Agent Architect

References

Get your agent architecture reviewed

Patterns

Design Patterns & Techniques

Loading patterns…

Design Patterns & Techniques

30-Second Overview

Quick Implementation

Do's & Don'ts

When to Use

Use When

Avoid When

Key Metrics

Top Use Cases

References & Further Reading

Foundational Papers

Theoretical Understanding (2022-2024)

Example Selection & Optimization

Chain-of-Thought & Reasoning

Recent Advances (2023-2024)

Multimodal In-Context Learning

Implementation Resources

Datasets & Benchmarks

Tools & Libraries

Analysis & Evaluation

Community & Discussion

Design Patterns & Techniques