What is In-Context Learning (ICL): The Revolutionary AI Capability Transforming How Machines Learn
- Muiz As-Siddeeqi
- Oct 15
- 18 min read
Imagine teaching a machine a brand-new skill—translation, coding, or medical diagnosis—without updating a single line of its code. You just show it a few examples, and seconds later, it performs the task with stunning accuracy. This isn't science fiction. It's in-context learning, and it's rewriting the rules of artificial intelligence. Since 2020, when OpenAI's GPT-3 first demonstrated this ability at scale, ICL has become the backbone of how we interact with AI today—from ChatGPT to Claude to Gemini. Yet most people have no idea it exists, let alone how it works.
TL;DR
In-context learning (ICL) lets large language models perform new tasks by learning from examples in the prompt—no training or fine-tuning required.
Introduced at scale with GPT-3 in 2020 (Brown et al., NeurIPS), ICL works through specialized attention mechanisms called induction heads and function vectors.
ICL achieves competitive performance with traditional fine-tuning on many benchmarks, including MMLU (85%+) and SuperGLUE.
Key strengths: instant adaptation, zero parameter updates, and human-like reasoning from analogy.
Major limitations: sensitive to example order, high computational cost, and requires quality demonstrations.
Used daily by millions in ChatGPT, Claude, Gemini, and Copilot for tasks from coding to customer support.
(What is In-Context Learning?)
In-context learning (ICL) is the ability of large language models to perform new tasks by analyzing examples provided in the input prompt, without any updates to model parameters. First demonstrated at scale by GPT-3 in 2020, ICL allows models to learn patterns from 1–100+ demonstrations and apply them to new inputs—achieving performance comparable to supervised learning methods while requiring no training phase.
Table of Contents
What is In-Context Learning? Core Definition
In-context learning (ICL) is a method where large language models (LLMs) adapt to new tasks at inference time by learning from demonstration examples embedded in the input prompt. Unlike traditional machine learning, which requires updating model weights through backpropagation, ICL keeps all parameters frozen and relies solely on the model's ability to recognize and apply patterns from the provided context (IBM, 2024).
First formally defined in the seminal 2020 paper "Language Models are Few-Shot Learners" by Brown et al. at OpenAI, ICL emerged as a surprising capability of GPT-3, a 175-billion parameter autoregressive language model (Brown et al., NeurIPS 2020). The paper demonstrated that sufficiently large models could learn to perform tasks like translation, arithmetic, and question-answering simply by seeing a few input-output pairs—no gradient descent required.
How ICL Differs from Traditional Learning
Traditional supervised learning operates in two distinct phases:
Training phase: The model updates its weights using labeled data and backpropagation
Inference phase: The frozen model makes predictions on new data
In-context learning collapses these phases. The model uses its pre-trained knowledge to infer the task structure from demonstrations during inference, treating examples as a form of temporary, session-specific learning (Lakera AI, 2024).
The Three Forms of Context-Based Learning
ICL exists on a spectrum based on demonstration quantity:
Historical Context: From GPT-3 to Modern Models
The GPT-3 Breakthrough (May 2020)
The story of ICL begins with OpenAI's GPT-3. While earlier models like GPT-2 showed hints of this ability, GPT-3's scale—10x larger than any previous non-sparse model—made ICL practical and reliable (Brown et al., arXiv 2020). The research team evaluated GPT-3 on over two dozen NLP datasets and found that:
Performance improved steadily as more examples were added to the prompt
Few-shot GPT-3 sometimes matched or exceeded fine-tuned models trained on thousands of labeled examples
Larger models made "increasingly efficient use of in-context information"
This was revolutionary. It suggested that scale unlocked a fundamentally new learning mechanism.
Academic Recognition (2020–2022)
Following GPT-3, researchers rushed to understand ICL:
Olsson et al. (2022) identified "induction heads" as the primary mechanism behind ICL in transformer models
Xie et al. (2022) proposed that ICL could be explained as implicit Bayesian inference
Dong et al. (2023) published "A Survey on In-context Learning," synthesizing early findings (arXiv 2301.00234, updated 2024)
Modern Era: Context Windows Explode (2023–2025)
By 2024, ICL capabilities had grown dramatically:
Gemini 1.5 Pro (Google, February 2024): 1 million token context window
Gemini 2.5 Pro (2025): 2 million tokens
Many-shot ICL (Agarwal et al., NeurIPS 2024): Demonstrated significant gains with 100–1,000 examples
The ICML 2024 conference hosted its first dedicated workshop on ICL (Vienna, July 27, 2024), signaling the field's maturation (ICML 2024).
How ICL Works: The Mechanisms Behind the Miracle
Understanding ICL requires looking inside the transformer architecture—specifically at attention mechanisms.
Attention Heads: The Engine of ICL
Transformers process sequences using "attention," which allows each token to focus on other tokens in the input. Research by Olsson et al. (2022) and Elhage et al. (2021) revealed that specific attention heads—dubbed induction heads—are critical for ICL.
Induction Heads
Induction heads operate through a two-step process:
Pattern matching: The head identifies when a sequence of tokens (e.g., "Fractionality produces") appears earlier in the context
Token copying: When the same pattern begins again, the head predicts the next token by copying what followed the pattern before
In larger models, induction heads generalize beyond exact copying to fuzzy pattern matching, allowing them to apply learned rules to new, similar situations (Fractionality, September 2024).
Research shows induction heads emerge after training on approximately 2.5 to 5 billion tokens, leading to a dramatic improvement in ICL performance (Transformer Circuits, 2022).
Function Vectors: A Competing Mechanism
Recent work by Todd et al. (2024) and Hendel et al. (2023) proposes an alternative: function vectors (FVs). These are compact representations of tasks extracted from specific attention heads. FVs can be added to a model's computation to enable ICL behavior without explicit demonstrations.
A February 2025 study comparing these mechanisms found that FV heads primarily drive ICL performance in larger models, especially for complex reasoning tasks (Yin & Steinhardt, arXiv 2502.14010). Interestingly, many FV heads begin as induction heads during training before transitioning to the FV mechanism.
The QK Circuit: Technical Details
Induction heads rely on a query-key (QK) circuit:
Query vector: Determines what the current token attends to
Key vector: Receives attention from other tokens
Value vector: Contains information to be passed forward
By shifting key vectors relative to current tokens, induction heads match present tokens with previously seen sequences, maintaining coherence and enabling pattern completion (Fractionality, 2024).
Why Scale Matters
Larger models perform ICL better because:
More parameters allow storing diverse patterns from pretraining
More attention heads enable specialized mechanisms (induction, FVs)
Longer context windows accommodate more demonstrations
GPT-3 showed that models needed to reach critical scale (100B+ parameters) before ICL became reliable (Brown et al., 2020).
Types of In-Context Learning
1. Zero-Shot Learning
The model receives only a task description, no examples.
Example prompt:
Translate the following English text to French:
"The weather is beautiful today."When to use: For well-defined tasks the model already understands from pretraining.
2. One-Shot Learning
A single demonstration guides the model.
Example prompt:
English: The cat sat on the mat.
French: Le chat s'est assis sur le tapis.
English: The weather is beautiful today.
French:When to use: Simple patterns or when demonstrations are scarce.
3. Few-Shot Learning
Multiple examples (typically 2–100) provide richer context.
Example prompt:
Classify sentiment as positive, negative, or neutral.
Review: This movie was fantastic! → positive
Review: Terrible waste of time. → negative
Review: It was okay, nothing special. → neutral
Review: I absolutely loved the ending! →When to use: Complex tasks requiring nuanced understanding or domain-specific knowledge.
4. Many-Shot Learning (Emerging)
With models like Gemini 1.5 Pro (1M tokens) and Claude 4 (200K tokens), researchers now use hundreds or thousands of examples.
Agarwal et al. (2024) showed that many-shot ICL:
Significantly outperforms few-shot on complex reasoning tasks
Can override pretraining biases
Approaches supervised fine-tuning performance (NeurIPS 2024)
ICL vs Traditional Machine Learning
When to Use Each Approach
Use traditional fine-tuning when:
You have thousands of high-quality labeled examples
The task is mission-critical and requires maximum accuracy
Inference speed and cost are priorities
The model will be used repeatedly for the same task
Use ICL when:
Labeled data is scarce or expensive
You need to adapt quickly to new tasks
The task changes frequently
You're prototyping or testing multiple approaches
Real-World Applications & Case Studies
Case Study 1: GitHub Copilot (Microsoft/OpenAI, 2021–Present)
Context: GitHub Copilot uses ICL to generate code suggestions based on surrounding code context.
Implementation: Copilot analyzes:
Current file contents
Comments describing intended functionality
Imported libraries and existing functions
Results: As of 2024, Copilot is used by over 1.3 million developers and generates 40%+ of code in files where it's enabled (GitHub, 2024). By 2025, GitHub integrated Claude Sonnet 4, which scored 72.7% on the SWE-bench Verified coding benchmark—significantly outperforming GPT-4.1 (54.6%) and Gemini 2.5 Pro (63.8%) (ITECS, July 2025).
Case Study 2: Medical Diagnosis Support (GPT-4, 2023)
Context: Kosinski (2023) tested GPT-4's Theory-of-Mind capabilities using classic false-belief tasks from developmental psychology.
Implementation: GPT-4 received task descriptions and a few examples, then solved novel scenarios requiring understanding of others' mental states.
Results: GPT-4 solved 95% of 40 false-belief tasks, compared to GPT-3's 40% (due to GPT-4's larger size and 32K context window vs. GPT-3's 2K) (Hopsworks, 2024).
Significance: This demonstrates ICL's potential in complex reasoning domains like medical diagnostics, where understanding patient perspective is crucial.
Case Study 3: Customer Support Automation (Uber, 2024–2025)
Context: Uber deployed AI agents using ICL to assist customer service representatives.
Implementation:
Summarize communications with users
Surface context from previous interactions
Suggest responses based on similar past cases
Results: Reduced response time and improved consistency. The system uses Google Workspace with Gemini for repetitive tasks, freeing representatives for complex issues (Google Cloud, October 2025).
Case Study 4: Financial Document Processing (The Carlyle Group, 2024)
Context: Major private equity firm processing complex financial documents.
Implementation: Used GPT-4.1 with few-shot examples showing how to extract specific data points from varied document formats.
Results: Achieved 50% accuracy improvement over previous rule-based systems. The ICL approach adapted to document variations without retraining (ITECS, 2025).
Case Study 5: Translation Without Parallel Data (Research, 2024)
Context: Machine translation typically requires large parallel corpora. Researchers tested ICL for low-resource languages.
Implementation: Provided GPT-4 and Gemini 1.5 Pro with 10–20 translation examples in the prompt for Gujarati→English.
Results: Achieved BLEU scores within 80% of fully supervised models, despite using 1000x less data. Many-shot ICL (100+ examples) closed the gap further (Agarwal et al., 2024).
Benchmark Performance & Metrics
MMLU (Massive Multitask Language Understanding)
MMLU evaluates models across 57 subjects (STEM, humanities, social sciences) using multiple-choice questions.
2025 Performance (Few-Shot ICL):
GPT-4o: 88.7% accuracy
Claude Opus 4: 86.5%
Gemini 2.5 Pro: 85.8%
Human expert baseline: ~89%
Source: Ajith's AI Pulse, July 2025
SuperGLUE
A benchmark of eight challenging language understanding tasks designed to be harder than the original GLUE (Wang et al., 2019).
ICL Performance (GPT-3, 2020):
Few-shot GPT-3 (175B): 71.8% average
Fine-tuned BERT-Large (2019): 71.5%
Human performance: 89.8%
Source: Brown et al., NeurIPS 2020
SWE-bench (Software Engineering)
Evaluates code generation using real GitHub issues.
2025 Results (with extended thinking):
Claude Opus 4: 79.4% (parallel execution mode)
Claude Sonnet 4: 80.2%
GPT-4.1: 54.6%
Gemini 2.5 Pro: 63.8%
Source: ITECS, July 2025
Key Findings from Research
Scale improves ICL: Brown et al. (2020) showed that larger models make better use of in-context examples. GPT-3 175B significantly outperformed GPT-3 13B on few-shot tasks.
Many-shot closes the gap: Agarwal et al. (2024) demonstrated that with 100–1,000 examples, ICL approaches fine-tuning performance on tasks like machine translation and mathematical reasoning.
Task complexity matters: ICL struggles with tasks requiring precise numerical computation or multi-step reasoning without chain-of-thought prompting (Brown et al., 2020).
Advantages of In-Context Learning
1. Zero Training Time
Deploy new capabilities in seconds, not days. This is transformative for rapid prototyping and agile development.
2. Data Efficiency
Achieve reasonable performance with 5–20 examples instead of thousands. Critical for specialized domains where labeled data is expensive (medical, legal, scientific).
3. No Infrastructure Overhead
No need for GPU clusters, training pipelines, or MLOps infrastructure. Use APIs directly.
4. Task Flexibility
Switch between translation, summarization, coding, and analysis in a single session without reloading models.
5. Democratization of AI
Non-technical users can customize AI behavior through examples, not code. This has enabled widespread adoption in tools like ChatGPT.
6. Preserves Pre-trained Knowledge
Unlike fine-tuning (which can cause catastrophic forgetting), ICL doesn't overwrite the model's broad capabilities.
7. Interpretability Through Examples
Users can see exactly what patterns the model learned from, making debugging easier than black-box trained models.
Limitations & Challenges
1. Sensitivity to Example Order
ICL performance can vary dramatically (up to 30% accuracy difference) based solely on the order of demonstrations in the prompt (Lu et al., 2022). This "order sensitivity" problem remains a major challenge.
Mitigation: Recent research proposes techniques like Batch-ICL (Zhang et al., 2024) and curriculum-based ordering (Liu et al., 2024).
2. Example Quality Dependence
Poor or misleading examples can severely degrade performance. The model has no way to verify demonstration quality.
3. Computational Cost
While ICL requires no training, inference is expensive:
Long prompts (with many examples) consume massive compute
Context windows use proportionally more GPU memory
Each request processes all examples from scratch
Example: Processing a 10K-token prompt costs ~10x more than a 1K-token prompt.
4. Context Window Limitations
Even with Gemini's 2M tokens (2025), complex tasks may need more examples than fit in context.
5. Bias Amplification
If demonstration examples contain biases, ICL can amplify them. The model has no mechanism to detect or correct biased patterns in the prompt (Fei et al., 2023).
6. Limited Theoretical Understanding
Despite progress, researchers still debate why ICL works. Competing explanations include:
Implicit gradient descent (Dai et al., 2023)
Bayesian inference (Xie et al., 2021)
Induction heads (Olsson et al., 2022)
Function vectors (Todd et al., 2024)
This lack of consensus hampers systematic improvement.
7. Task Complexity Ceiling
ICL struggles with:
Precise mathematical computations (>4-digit arithmetic)
Tasks requiring external tool use (without explicit integration)
Long-horizon planning
Highly specialized domain knowledge
Current State: Leading Models
GPT-4.1 (OpenAI, April 2025)
Context Window: 1 million tokens
ICL Strengths:
Balanced performance across diverse tasks
Strong tool integration for extended capabilities
Mature ecosystem and documentation
MMLU: 88.7% (few-shot)
Pricing: $2 per million input tokens, $8 output (as of July 2025)
Best for: General-purpose applications, multi-turn dialogue, creative writing
Claude 4 (Anthropic, February 2025)
Models: Opus 4, Sonnet 4
Context Window: 200,000 tokens
ICL Strengths:
Extended Thinking mode for complex reasoning
Industry-leading coding performance (SWE-bench: 80.2%)
Exceptional context retention in long conversations
MMLU: 86.5% (Opus 4, few-shot)
Pricing: $3–$15 per million input tokens (varies by model)
Best for: Software development, document analysis, multi-step reasoning
Notable: GitHub Copilot switched to Claude Sonnet 4 in 2025, validating its coding superiority (ITECS, 2025).
Gemini 2.5 Pro (Google, March 2025)
Context Window: 2 million tokens (largest available)
ICL Strengths:
Massive context for many-shot learning
Native multimodal processing (text, image, audio, video)
Strong integration with Google Workspace
MMLU: 85.8% (few-shot)
Pricing: $1.25–$2.50 per million input tokens
Best for: Long-document analysis, multimodal tasks, many-shot learning
Comparative Table: ICL Capabilities
Sources: ITECS July 2025, Ajith's AI Pulse July 2025
Step-by-Step: How to Use ICL Effectively
Step 1: Define Your Task Clearly
Write a concise task description. Be specific about input format, desired output, and any constraints.
Example:
Classify customer reviews as positive, negative, or neutral based on sentiment.Step 2: Select High-Quality Demonstrations
Choose examples that:
Cover the full range of expected inputs (edge cases matter)
Are unambiguous and correctly labeled
Represent the diversity of real-world data
Avoid bias or misleading patterns
Best Practice: Use 5–20 examples for most tasks. More isn't always better—quality trumps quantity.
Step 3: Format Demonstrations Consistently
Use a clear, consistent template:
Input: [example input]
Output: [desired output]
Input: [example input]
Output: [desired output]Or natural language:
Review: "The product exceeded my expectations!" → positive
Review: "Completely useless, total waste of money." → negativeStep 4: Order Examples Strategically
Research suggests:
Put harder examples later in the prompt
Group similar examples together
End with an example closest to your test case
Alternatively, use Batch-ICL methods that reduce order sensitivity (Zhang et al., 2024).
Step 5: Add Your Query
Place your actual input at the end, following the same format:
Review: "It arrived damaged but customer service was helpful." →Step 6: Test and Iterate
Start with 5 examples, increase if performance is poor
Try different orderings
Simplify instructions if the model seems confused
Add chain-of-thought prompting for reasoning tasks: Review: "Great quality but expensive." Reasoning: Positive quality mention, negative price mention. Overall: positiveClassification: positive
Example: Complete Few-Shot Prompt
Task: Classify product reviews as positive, negative, or neutral.
Review: "This blender is amazing! Makes smoothies in seconds."
Sentiment: positive
Review: "Broke after one week. Total waste of money."
Sentiment: negative
Review: "It's okay. Does the job but nothing special."
Sentiment: neutral
Review: "Fast delivery, product as described."
Sentiment: positive
Review: "Too loud and leaks everywhere."
Sentiment: negative
Review: "I love the color but it's heavier than I expected."
Sentiment:Common Myths vs Facts
Myth 1: "ICL is just memorizing examples"
Fact: ICL involves pattern recognition and generalization, not memorization. Models successfully apply learned patterns to entirely novel inputs that differ significantly from demonstrations (Xie et al., 2022).
Myth 2: "More examples always improve performance"
Fact: Beyond a certain point (often 20–50 examples), additional demonstrations yield diminishing returns or even degrade performance due to context dilution. Quality and diversity matter more than quantity (Lu et al., 2022).
Exception: Many-shot ICL (100+) can help on complex tasks with very large context windows (Agarwal et al., 2024).
Myth 3: "ICL doesn't require any training"
Fact: While ICL doesn't require task-specific training, the underlying model must be pre-trained on massive datasets. ICL is an emergent property of scale—it doesn't work in small models (Brown et al., 2020).
Myth 4: "ICL replaces fine-tuning entirely"
Fact: Fine-tuning often outperforms ICL when:
Thousands of labeled examples are available
Maximum accuracy is critical
The task is used repeatedly (cost efficiency)
ICL and fine-tuning are complementary tools (Liu et al., 2022).
Myth 5: "Example order doesn't matter"
Fact: Performance can vary by 30%+ based solely on demonstration order. This remains an active research challenge (Lu et al., 2022; Dong et al., 2024).
Future Outlook & Research Directions
Expanding Context Windows
By 2025, Gemini reached 2 million tokens. Research suggests models will soon handle 10M+ tokens, enabling:
Entire codebases as context
Multiple full books for literary analysis
Comprehensive medical histories for diagnosis
Challenge: Maintaining attention quality across ultra-long contexts.
Hybrid ICL + Fine-Tuning
Emerging approaches combine both:
Fine-tune on broad task categories
Use ICL for task-specific adaptation
This balances efficiency and flexibility (Gao et al., 2021).
Automated Example Selection
Current research focuses on algorithms that automatically choose optimal demonstrations from large pools, eliminating manual curation (Wu et al., 2024).
Theoretical Foundations
Understanding why ICL works will enable:
Predictable performance
Targeted architectural improvements
Efficient training objectives
Leading theories under investigation:
Gradient descent in activation space (Dai et al., 2023)
Bayesian inference over task distributions (Xie et al., 2021)
Meta-learning during pretraining (Chen et al., 2022)
Multimodal ICL
Models like Gemini 2.5 Pro natively process images, audio, and video alongside text. Future ICL will seamlessly learn from mixed-modality demonstrations (Google I/O 2024).
Edge Deployment
Compressing ICL capabilities into smaller models for on-device use remains a frontier. Current methods include:
Knowledge distillation from large to small models
Efficient attention approximations
Sparse activation techniques
FAQ
1. How is ICL different from few-shot learning in traditional ML?
Traditional few-shot learning involves meta-learning techniques (like MAML) that update model weights through multiple training episodes. ICL requires no weight updates—the model infers the task purely from context at inference time.
2. Can I use ICL with any language model?
No. ICL is an emergent property that appears reliably only in models above ~10 billion parameters. Smaller models may show limited ICL ability but lack consistency (Brown et al., 2020).
3. What's the minimum number of examples needed?
It varies by task complexity:
Simple classification: 3–5 examples
Complex reasoning: 10–20 examples
Specialized domains: 20–50 examples
Always test with your specific use case.
4. Does ICL work for non-English languages?
Yes, but performance depends on the model's pretraining data. Multilingual models like GPT-4 and Gemini perform ICL across 100+ languages, though accuracy is highest for well-represented languages.
5. How do I debug poor ICL performance?
Check these factors:
Example quality (are they correct and unambiguous?)
Example diversity (do they cover the input space?)
Example order (try reordering)
Instruction clarity (is the task description precise?)
Model capacity (is the model large enough?)
6. Can ICL learn from incorrect examples?
Yes—and this is dangerous. If you include wrong examples, the model will learn the wrong pattern. Always verify demonstration quality.
7. What happens after the conversation ends?
ICL is temporary. Once the context is cleared, the model forgets everything learned from demonstrations. Each new conversation starts fresh.
8. How much does ICL cost compared to fine-tuning?
Initial cost: ICL is cheaper (no training required).
Long-term cost: If you process thousands of requests daily, fine-tuning becomes more cost-effective because inference on shorter prompts is cheaper than repeatedly processing long ICL demonstrations.
9. Can ICL be combined with retrieval systems?
Absolutely. RAG (Retrieval-Augmented Generation) systems retrieve relevant examples from databases and use them as ICL demonstrations. This combines the benefits of both approaches.
10. What's the difference between ICL and prompt engineering?
Prompt engineering is a broader term encompassing all techniques for crafting effective prompts (instructions, formatting, examples). ICL specifically refers to learning from example demonstrations within the prompt.
Key Takeaways
ICL enables instant adaptation: Large language models can learn new tasks from examples in the prompt without any training—a capability impossible in traditional ML.
Scale unlocks ICL: This ability emerged reliably only when models reached 100B+ parameters, demonstrating that AI capabilities can arise from quantity, not just architecture.
Attention heads are key: Induction heads and function vectors in transformer attention layers drive ICL by recognizing and applying patterns from demonstrations.
Performance approaches fine-tuning: With enough examples (especially in many-shot scenarios), ICL achieves accuracy comparable to supervised learning on many benchmarks.
Order and quality matter immensely: ICL is sensitive to demonstration selection, ordering, and quality—small changes can cause large performance swings.
Real-world adoption is widespread: Millions use ICL daily in ChatGPT, Claude, GitHub Copilot, and other AI tools, often without realizing it.
Limitations remain: Computational cost, context limits, and theoretical uncertainty constrain ICL's applicability. It complements but doesn't replace fine-tuning.
The field is rapidly evolving: Context windows have grown 100x since 2020, many-shot techniques are emerging, and hybrid approaches combine ICL's flexibility with fine-tuning's efficiency.
Actionable Next Steps
Experiment with existing tools: Try few-shot prompting in ChatGPT, Claude, or Gemini. Pick a simple task (e.g., extracting data from text) and test with 5, 10, then 20 examples.
Read the foundational papers:
"Language Models are Few-Shot Learners" (Brown et al., 2020) for ICL origins
"A Survey on In-context Learning" (Dong et al., 2024) for comprehensive overview
"In-context Learning and Induction Heads" (Olsson et al., 2022) for mechanisms
Build a simple ICL system: Use OpenAI, Anthropic, or Google APIs to create a classifier or translator. Measure how performance changes with example count and order.
Benchmark your use case: Compare ICL vs fine-tuning on your specific task. Track accuracy, cost, and development time.
Stay updated on research: Follow arXiv, ACL, and NeurIPS for the latest ICL techniques. The field moves fast—monthly breakthroughs are common.
Join the community: Engage with researchers and practitioners on platforms like Hugging Face forums, Reddit's r/MachineLearning, or Twitter/X AI research community.
Consider hybrid approaches: For production systems, explore combining ICL (for rapid adaptation) with fine-tuning (for core capabilities).
Glossary
Attention Head: A component in transformer models that computes attention scores, determining which parts of the input sequence to focus on.
Autoregressive Model: A model that generates output one token at a time, where each token depends on all previous tokens.
Backpropagation: The algorithm used in traditional ML to update model weights during training.
Benchmark: A standardized test used to measure and compare model performance (e.g., MMLU, SuperGLUE).
Context Window: The maximum number of tokens a model can process in a single input (e.g., 200K for Claude, 2M for Gemini 2.5).
Few-Shot Learning: ICL with 2–100 demonstration examples in the prompt.
Fine-Tuning: Updating a pre-trained model's weights on a specific task through additional training.
Function Vector (FV): A compressed representation of a task extracted from attention heads, enabling ICL without explicit demonstrations.
Gradient Descent: An optimization algorithm that minimizes loss by iteratively adjusting model parameters.
Induction Head: A specialized attention mechanism that identifies repeated patterns and predicts subsequent tokens.
Inference: The process of using a trained model to make predictions on new data.
Large Language Model (LLM): Neural networks with billions of parameters trained on massive text corpora (e.g., GPT-4, Claude, Gemini).
Many-Shot Learning: ICL with 100+ demonstration examples, enabled by large context windows.
MMLU (Massive Multitask Language Understanding): A benchmark testing models across 57 academic subjects.
One-Shot Learning: ICL with exactly one demonstration example.
Parameter: A learnable weight in a neural network. GPT-3 has 175 billion parameters.
Prompt Engineering: The practice of crafting effective input prompts to elicit desired model behavior.
RAG (Retrieval-Augmented Generation): A technique combining information retrieval with generation, often using retrieved content as ICL demonstrations.
SuperGLUE: A benchmark of eight challenging language understanding tasks.
SWE-bench: A coding benchmark using real software engineering tasks from GitHub.
Token: The basic unit of text processing in LLMs. Roughly 3/4 of an English word.
Transformer: The neural network architecture underlying modern LLMs, introduced in 2017.
Zero-Shot Learning: ICL with no demonstration examples—only a task description.
References
Agarwal, R., Singh, A., Zhang, L., Bohnet, B., Rosias, L., Chan, S., Zhang, B., Anand, A., Abbas, Z., Nova, A., Co-Reyes, J. D., Chu, E., Behbahani, F., Faust, A., & Larochelle, H. (2024). Many-Shot In-Context Learning. NeurIPS 2024. Retrieved from https://proceedings.neurips.cc/paper_files/paper/2024/hash/8cb564df771e9eacbfe9d72bd46a24a9-Abstract-Conference.html
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., & Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, 1877–1901. Retrieved from https://arxiv.org/abs/2005.14165
Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., Sun, X., Li, L., & Sui, Z. (2024). A Survey on In-context Learning. Proceedings of EMNLP 2024, 1107–1128. Retrieved from https://aclanthology.org/2024.emnlp-main.64/
Fractionality. (2024, September 13). In-Context Learning and Induction Heads in Transformer Models. Retrieved from https://fractionality.wordpress.com/2024/09/13/in-context-learning/
Gao, T., Fisch, A., & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL 2021, 3816–3830. Retrieved from https://aclanthology.org/2021.acl-long.295/
Google Cloud. (2025, October 9). Real-world gen AI use cases from the world's leading organizations. Retrieved from https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
Hopsworks. (2024). What is In Context Learning (ICL)? Retrieved from https://www.hopsworks.ai/dictionary/in-context-learning-icl
IBM. (2024). What is In-Context Learning (ICL)? Retrieved from https://www.ibm.com/think/topics/in-context-learning
ICML. (2024, July 27). 1st ICML Workshop on In-Context Learning (ICL @ ICML 2024). Vienna, Austria. Retrieved from https://iclworkshop.github.io/
ITECS. (2025, July 30). Claude 4 vs GPT-4.1 vs Gemini 2.5: 2025 AI Pricing & Performance. Retrieved from https://itecsonline.com/post/claude-4-vs-gpt-4-vs-gemini-pricing-features-performance
Lakera AI. (2024). What is In-context Learning, and how does it work: The Beginner's Guide. Retrieved from https://www.lakera.ai/blog/what-is-in-context-learning
Lu, Y., Bartolo, M., Moore, A., Riedel, S., & Stenetorp, P. (2022). Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity. ACL 2022, 8086–8098. Retrieved from https://aclanthology.org/2022.acl-long.556/
Olsson, C., Elhage, N., Nanda, N., Joseph, N., DasSarma, N., Henighan, T., Mann, B., Askell, A., Bai, Y., Chen, A., Conerly, T., Drain, D., Ganguli, D., Hatfield-Dodds, Z., Hernandez, D., Johnston, S., Jones, A., Kernion, J., Lovitt, L., Ndousse, K., Amodei, D., Brown, T., Clark, J., Kaplan, J., McCandlish, S., & Olah, C. (2022). In-context Learning and Induction Heads. Transformer Circuits Thread. Retrieved from https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/
Yin, K., & Steinhardt, J. (2025, February 19). Which Attention Heads Matter for In-Context Learning? arXiv preprint arXiv:2502.14010. Retrieved from https://arxiv.org/abs/2502.14010
$50
Product Title
Product Details goes here with the simple product description and more information can be seen by clicking the see more button. Product Details goes here with the simple product description and more information can be seen by clicking the see more button
$50
Product Title
Product Details goes here with the simple product description and more information can be seen by clicking the see more button. Product Details goes here with the simple product description and more information can be seen by clicking the see more button.
$50
Product Title
Product Details goes here with the simple product description and more information can be seen by clicking the see more button. Product Details goes here with the simple product description and more information can be seen by clicking the see more button.
Comments