Zero-shot prompting asks a language model to perform a task without demonstrations. For a simple baseline in intent classification, use a prompt without examples; demonstrations can strengthen prompts, while prompt optimization is deferred. section 3.2
Zero-shot prompting
Zero-shot prompting is a prompting technique that asks a language model to perform a task without providing task-specific demonstrations.
How Building LLM Applications with DSPy covers it
- How deep it goes
- Defines prompting by the absence of demonstrations, contrasts one-shot and few-shot approaches, and defers optimization.
- Examples
- Uses ATIS intent classification as a baseline context and proceeds with test data without training or validation data.
- What you can do after
- Apply zero-shot prompting as a no-demonstration baseline for intent classification using test data alone.
- How it connects
- Contrasts zero-shot prompting with one-shot and few-shot prompting and connects optimization to demonstrations and validation data.
Also explained in
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AI for Everyday ITZero-shot prompting is the technique of asking an AI model to perform a task without providing prior examples or task-specific context.
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The Art of AI Product DevelopmentZero-shot prompting is prompting a model to perform a task without providing example input-output pairs in the prompt.
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LLMs in ProductionZero-shot prompting is prompting a model to perform a task without providing task-specific examples in the prompt.
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Effective Conversational AIA zero-shot prompt is a prompt that specifies a task without providing an example of the desired input-output behavior.
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Learn Generative AI with PyTorchA prompting method that gives the model a task without any examples, relying only on instructions and prior knowledge.
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Prompt Engineering in PracticeZero-shot prompting is a prompting technique that asks a model to perform a task without providing task examples in the prompt.
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Time Series Forecasting Using Foundation ModelsZero-shot prompting is prompting a model to perform a task without providing task-specific examples in the prompt.
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Building Reliable AI SystemsZero-shot prompting is instructing a model to perform a task without providing task-specific examples in the prompt.
Compare how 9 books explain zero-shot prompting →
Related concepts
- prompt engineeringkind of · Prompt engineering is the deliberate design and refinement of instructions, context, examples, or other inputs to guide an AI model’s behavior or outputs.
- language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- few-shot learningcontrasts with · Few-shot learning is the adaptation or performance of a model on a task using only a small number of task-specific examples.
- few-shot prompt contextcontrasts with · Few-shot prompt context is input to a model that includes a small number of examples to guide its response or behavior.
- chain-of-thought promptingused by · Chain-of-thought prompting is a technique that elicits or guides a model to produce or follow intermediate reasoning steps before a final answer.
This explainer was generated by Manning's knowledge graph from the text of Building LLM Applications with DSPy. The section links point to the parts of the book it draws on.