What is In-Context Learning?
In-Context Learning is the ability of AI models to adapt their behavior and learn new tasks based on the information, examples, and instructions provided within the prompt itself, without any modification to the underlying model, enabling real-time customization of AI outputs for specific business needs.
What Is In-Context Learning?
In-Context Learning (ICL) is the phenomenon where AI models adapt their behavior based on the content of the prompt they receive, effectively "learning" how to perform a task from the context provided in a single interaction. The model's underlying parameters do not change -- instead, it uses the information, examples, patterns, and instructions within the prompt to guide its response.
This concept encompasses both few-shot learning (providing examples in the prompt) and the broader ability of AI models to absorb and apply information given to them in real time. If you paste a company document into a prompt and ask the AI to answer questions about it, the AI has "learned" from that document within the context of your interaction -- even though it had no prior knowledge of that specific document.
For business leaders, in-context learning is the mechanism that makes AI tools so versatile. It explains why the same AI model can draft marketing copy in the morning, analyze financial data at lunch, and review legal documents in the afternoon. The model adapts to each task based on the context you provide, not because it was specifically trained for each scenario.
How In-Context Learning Works
When you interact with an AI model, everything in the prompt becomes the model's working context:
Instructions tell the model what task to perform and how to approach it. Clear, detailed instructions are the most basic form of in-context learning guidance.
Examples demonstrate the desired input-output pattern. This is the few-shot learning approach, where the model infers the pattern from examples and applies it to new inputs.
Reference material provides domain knowledge the model can draw from. Pasting a product manual, policy document, or dataset into the prompt gives the model information it can use to produce more accurate, relevant responses.
Conversation history in multi-turn interactions allows the model to build understanding over the course of a conversation. Earlier exchanges inform later responses, enabling the model to refine its understanding of what you need.
The key insight is that the model does all of this without any permanent learning. Once the interaction ends, the model retains nothing from your session. Each new conversation starts with a blank slate unless you provide the context again. This is both a limitation (you need to re-establish context each time) and a security feature (your data is not permanently stored in the model).
Why In-Context Learning Matters for Business
Instant Customization Without Technical Investment In-context learning means you can customize an AI's behavior for any task simply by providing the right context in your prompt. No training data pipelines, no model fine-tuning, no machine learning engineers. This is particularly valuable for SMBs that need AI flexibility without the resources for custom model development.
Data-Driven Responses By providing relevant business data within the prompt, you can get AI responses grounded in your actual information rather than the model's general knowledge. Upload your quarterly sales data and ask for analysis, paste your employee handbook and ask policy questions, or provide your product catalog and generate descriptions -- the model works with your data in real time.
Dynamic Adaptation Unlike fine-tuned models that behave the same way until retrained, in-context learning allows immediate adaptation. Changed your product pricing? Provide the new pricing in the prompt. Updated your company policies? Include the new policies. The AI adapts instantly to the most current information you provide.
Practical Applications Across ASEAN Markets
Company Knowledge Bases Modern AI applications use in-context learning to create dynamic knowledge assistants. By loading relevant company documents into the AI's context (often through a technique called RAG -- Retrieval-Augmented Generation), businesses create assistants that answer questions based on their specific policies, products, and procedures.
Market-Specific Content Generation Provide the AI with information about a specific ASEAN market -- demographics, cultural preferences, local competitors, regulatory requirements -- and it can generate content and analysis tailored to that market. Switch markets by changing the context, without building separate AI systems for each country.
Real-Time Data Analysis Business analysts can paste data into AI conversations and receive instant analysis, visualizations recommendations, and insights. The AI learns the structure and content of the data from the context, requiring no prior setup.
Customer Interaction Enhancement By providing customer history, purchase data, and communication preferences in the context, AI can generate personalized responses and recommendations for each customer interaction.
Document Processing Legal, financial, and operational documents can be provided in context for summarization, comparison, key term extraction, and compliance analysis. The AI adapts to each document's structure and content automatically.
Maximizing In-Context Learning
To get the best results from in-context learning:
- Provide relevant context, not everything: Including too much irrelevant information can confuse the model. Be selective about what context supports the specific task.
- Structure your context clearly: Use headers, bullet points, and clear formatting to help the model parse the information you provide.
- Place the most important context near the beginning or end: Research shows models pay more attention to the start and end of long prompts.
- Be explicit about what to do with the context: Simply providing information is not enough -- tell the model exactly how to use it.
- Verify outputs against source material: The model may still generate information beyond what you provided, so verify that responses are grounded in your actual context.
In-Context Learning is the fundamental capability that makes modern AI tools practical for business use. Without it, AI models would be rigid systems capable only of the tasks they were specifically trained for. With it, the same AI model becomes a flexible tool that adapts to virtually any business task based on the context you provide. For CEOs and CTOs, this means a single AI investment can serve dozens of use cases across your organization.
The practical impact for SMBs in Southeast Asia is significant. Instead of building or buying specialized AI tools for each business function -- a separate tool for customer service, another for content generation, another for data analysis -- you can leverage in-context learning to make general-purpose AI models perform all these functions. This consolidation reduces technology complexity and costs while increasing flexibility.
Understanding in-context learning also helps business leaders make better decisions about when to invest in more advanced AI customization. If providing good context in prompts gives you 80 percent of the quality you need, the investment in fine-tuning to reach 90 percent may not be justified. For many SMB use cases, mastering in-context learning through better prompt design, structured context provision, and well-organized reference materials delivers more value per dollar than any other AI investment.
- Design your AI workflows to provide relevant business context within prompts rather than expecting the AI to know your business specifics from its general training
- Invest in organizing and structuring your business documents so they can be easily provided as context to AI tools when needed
- Understand context window limits -- each AI model can only process a certain amount of context, so prioritize the most relevant information for each task
- Use RAG (Retrieval-Augmented Generation) systems to automatically pull relevant documents into the AI context for knowledge-intensive applications
- Remember that in-context learning is temporary -- the AI does not retain information between sessions, which is both a privacy advantage and a workflow consideration
- Test whether the AI is actually using your provided context or falling back on its general knowledge, especially for domain-specific tasks where accuracy matters
Frequently Asked Questions
Does in-context learning mean the AI remembers our conversations?
No. In-context learning only works within a single conversation or prompt. Once the interaction ends, the model retains nothing from that session. Each new conversation starts fresh. This is actually a privacy advantage because your business data provided in one session is not accessible in future sessions or to other users. If you need the AI to "remember" information across sessions, you need to re-provide it each time or use application layers like RAG systems that automatically retrieve and include relevant information in each interaction.
Is there a limit to how much context we can provide?
Yes, every AI model has a context window limit that determines how much text it can process in a single interaction. This ranges from about 8,000 tokens (roughly 6,000 words) in older models to 200,000 tokens (roughly 150,000 words) in the latest models from Anthropic and Google. For most business tasks, even smaller context windows are sufficient. For tasks involving large documents or multiple reference files, choose models with larger context windows or use RAG systems that intelligently select the most relevant portions of your documents to include.
More Questions
In-context learning provides temporary guidance within a prompt -- the model adapts for that interaction only. Fine-tuning permanently modifies the model's parameters through additional training on your data. In-context learning is free, instant, and requires no technical expertise. Fine-tuning costs money, takes time, and requires technical resources. For most SMB use cases, in-context learning provides sufficient customization. Fine-tuning is worth considering only when you need highly specialized behavior that in-context learning consistently cannot achieve, such as deeply domain-specific language or complex reasoning patterns unique to your industry.
Need help implementing In-Context Learning?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how in-context learning fits into your AI roadmap.