Zero-shot prompting

As explained in Time Series Forecasting Using Foundation Models

Zero-shot prompting is prompting a model to perform a task without providing task-specific examples in the prompt.

Zero-shot prompting asks an LLM to perform a task using only a natural-language question or instruction, without task-specific examples. For forecasting, it reframes numerical prediction as question answering: provide past values and ask for the next value. section 8.1.2

A practical zero-shot forecasting prompt should:

• State the numerical input clearly. section 8.1.2
• Specify the requested operation, such as forecasting the next value. section 8.1.2
• Expect free-form output and add extraction logic when numerical values are required. section 8.1.2

Forecasting prompt

The past values are: 10, 15, 21, 26, 32, 28. What will be the next value? A possible response is: The next value will be 35. section 8.1.2

Figure 8.1 General steps of forecasting with LLMs

Use the workflow diagram to connect numerical forecasting inputs with their natural-language LLM prompt and generated response. section 8.1.2

How Time Series Forecasting Using Foundation Models covers it

How deep it goes

Defines the prompt pattern and shows one forecasting example, but defers output-format control to few-shot prompting.

Examples

Uses past values 10, 15, 21, 26, 32, 28 and asks for the next value, with 35 as a possible answer.

What it assumes

Assumes numerical forecasting inputs and teaches how to express them as a natural-language request.

What you can do after

Apply it via an LLM to frame forecasting requests, then extract numerical values from free-form answers.

How it connects

Connects zero-shot prompting to large language models, forecasting, few-shot prompting, and chain-of-thought prompting.

liveBookmanning.com

Read Time Series Forecasting Using Foundation Models on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  LLMs in ProductionZero-shot prompting is prompting a model to perform a task without providing task-specific examples in the prompt.
• 

  liveBookmanning.com
  Effective Conversational AIA zero-shot prompt is a prompt that specifies a task without providing an example of the desired input-output behavior.
• 

  liveBookmanning.com
  Learn Generative AI with PyTorchA prompting method that gives the model a task without any examples, relying only on instructions and prior knowledge.
• 

  liveBookmanning.com
  Building LLM Applications with DSPyZero-shot prompting is a prompting technique that asks a language model to perform a task without providing task-specific demonstrations.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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 Time Series Forecasting Using Foundation Models. The section links point to the parts of the book it draws on.