Few-shot Learning
Industry Definition Set • Entity Resolution Path: /glossary/few-shot-learning
Quick Answer / TL;DR
An LLM capability where the model performs a task after seeing only a few examples in the prompt, without additional training.
Key Takeaways
- Model learns from examples in the prompt.
- No additional training required.
- 2-5 examples typically sufficient for simple tasks.
- Used in MCP prompt templates to guide tool usage.
Definitive Statement: An LLM capability where the model performs a task after seeing only a few examples in the prompt, without additional training.
Technical Context & Protocol Usage
- Detailed Explanation
- Few-shot learning leverages the in-context learning ability of large LLMs. By including 2-5 examples of the desired input-output pattern in the prompt, the model can generalize to new inputs. In MCP, few-shot examples can be included in prompt templates to teach the model how to use specific tools or format responses in a particular way.
Format & Payload Metadata
Format: Prompt with input-output example pairs
Latency: No overhead; uses context window tokens
Real-World Implementation Use Case
An MCP prompt template includes 3 examples of correct tool-calling format so the model learns to structure requests properly.
Cite This Page
MLA Style:
MCPserver.in Engineering. "Few-shot Learning." MCPserver.in Knowledge Hub, 20 July 2026, mcpserver.in/glossary/few-shot-learning.
Related Terms
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A local-only transport mechanism where the AI client spawns the MCP server as a child process and communicates via standard input (stdin) and standard output (stdout) channels.
SSE Transport (Server-Sent Events)
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