Few-Shot Prompting
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What is Few-Shot Prompting?
Few-shot prompting is a technique used with large language models (LLMs) where you provide a small number of example input-output pairs in the prompt itself. These examples demonstrate the desired behavior of the LLM for a specific task. By seeing these "few shots" of correct answers, the LLM can better understand the pattern and generate accurate and relevant responses for new, unseen inputs.
Free resources to learn Few-Shot Prompting
More in stage 5: Type of Models
- Type of Models
- Pre-trained Models
- Closed vs Open Source Models
- Self-Hosted Models
- Zero-Shot
- ReAct
- CoT
- Function Calling
- Prompt Caching
- Streaming Responses
- RAG & Dynamic Filters
- Memory Systems
- Context Compaction
- Long-Context Processing
- State & Historical Context
- Multi-agent Context Sharing
- Context Isolation
- Context Failure Modes
Few-Shot Prompting: common questions
Can I learn Few-Shot Prompting in Hindi?
This lesson is written in English. Once you sign in, open it in the interactive course and ask the AI tutor to explain Few-Shot Prompting in Hindi or Hinglish. The tutor answers from this lesson’s own text, so it stays on the syllabus, and it runs on your own Google, OpenAI or Anthropic key.
Is this Few-Shot Prompting tutorial for beginners?
It is lesson 45 of 189 in the free AI Engineer roadmap, in stage 5, Type of Models. If you are new to AI Engineer, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.