Output format
An output format is a specified structure or representation in which a result is returned or produced.
Effective Data Analysis
A response format is the structure and set of options through which respondents provide answers to a question or assessment item.
- How it is used
- Structures answers to self-report questions about perceptions, attitudes, beliefs, and experiences for analytics decisions.
- How deep it goes
- Explains that format variations affect responses and recommends balanced scales with five or seven options, without detailing a testing procedure.
- Examples
- Uses contrasting software-survey questions and a figure comparing presentations of the same response format.
- What you can do after
- Practice/perform survey design by checking wording and choosing balanced response options before using answers to inform decisions.
Prompt Engineering in Practice
A structured output is a model result organized according to a predefined machine-readable or systematically validated structure.
- How it is used
- Integrates model responses directly into business workflows.
- How deep it goes
- Mentions workflow integration but does not detail schemas, validation, or output-format mechanics.
- Examples
- Provides no concrete structured-output example.
- What you can do after
- Apply structured outputs to integrate model responses into business workflows.
Read how Prompt Engineering in Practice explains output format →
Data Analysis with LLMs
Structured output is a result organized according to an explicit schema or format so that its components can be processed programmatically.
- How it is used
- Constrains model answers so review classifications can be aggregated and extracted attributes can be processed as records.
- How deep it goes
- Works through fixed labels and a delimited extraction template, then describes postprocessing raw answers into structured output.
- Examples
- Uses positive-or-negative movie reviews and a table template with boundary markers, pipe-separated fields, and missing-value placeholders.
- What you can do after
- Build it from scratch by specifying answer labels or table rules in prompts and postprocessing extraction results into CSV.
Reinforcement Learning from Human Feedback
Response format is the structural arrangement and presentation convention of a model response.
- How it is used
- Uses response structure to make instruction-following behavior and benchmark answers directly consumable and measurable.
- How deep it goes
- Defines format against tone, then traces templates, masking, exact-match constraints, and reasoning-answer separation.
- Examples
- Uses boiling-point answers, sleep advice layouts, multiple-choice letters, rigid answer suffixes, and a reasoning prompt with a final answer phrase.
- What you can do after
- Evaluate/understand how response-format choices alter instruction following, exact-match scoring, and benchmark comparisons.
Read how Reinforcement Learning from Human Feedback explains output format →
Effective Conversational AI
Structured output is generated information organized according to a predefined machine-readable format or set of fields.
- How it is used
- Uses predefined fields to make language-model results reviewable or consumable by downstream applications.
- How deep it goes
- Explains field selection, JSON prompting, one-shot examples, and format evaluation, but does not detail parser implementation.
- Examples
- Demonstrates intent-labeled utterances and transcript extraction of TaxID, MemberID, and ClaimID.
- What you can do after
- Apply it via prompt examples and explicit schemas to guide extraction and assess whether generated responses meet required formats.
Read how Effective Conversational AI explains output format →
AI Agents and Applications
An output format is a specification of the structure or representation in which a language model should return its answer.
- How it is used
- Use output-format instructions to specify the structure or representation expected from a response.
- How deep it goes
- Names structured formats and explains the text default, without detailing format validation.
- Examples
- Shows tables, CSV with column names, JSON with attribute names, and a two-paragraph, five-line layout.
- What you can do after
- Apply it by specifying a response layout, such as JSON with attribute names or a table with CSV column names.
Read how AI Agents and Applications explains output format →
The Art of AI Product Development
An output format is a specification of the structure or representation that a model-generated result must follow.
- How it is used
- Specify a response’s structure when downstream work needs to consume the result programmatically.
- How deep it goes
- Defines the component and illustrates it with a sectioned article outline.
- Examples
- Shows an article outline with an introduction, 3–5 body points, and a conclusion.
- What you can do after
- Apply it by stating the desired structure so generated content can be used programmatically.
Read how The Art of AI Product Development explains output format →
Coding with AI
An output format is the specified structural arrangement and representation that a generated response must follow.
- How it is used
- Guides AI-generated code and documents toward a requested structure rather than a generic response.
- How deep it goes
- Defines format choices and their role in prompting but does not specify a formal schema or implementation procedure.
- Examples
- Uses bullet points, code snippets, data structures, generated code, and technical documentation as output forms.
- What you can do after
- Apply it via prompt instructions that specify the desired response structure for a programming or documentation task.
Introduction to Generative AI, Second Edition
Structured output is machine-readable model output organized according to a specified format rather than unrestricted prose.
- How it is used
- Enables language-model agents to express tool calls or other actions alongside, or instead of, text responses.
- How deep it goes
- Explains action-shaped outputs, ways to teach them, and scaffolding’s effect, but gives no implementation details for a specific format.
- Examples
- Illustrates tool inputs and outputs with question answering, Wikipedia search, a calculator, a calendar, and machine translation.
- What you can do after
- Understand how language models can produce actions and how prompting or training can teach that behavior.
Read how Introduction to Generative AI, Second Edition explains output format →
Building Reliable AI Systems
An output organized according to a predefined schema so that both people and software can interpret its contents consistently.
- How it is used
- Sets response expectations as one component of prompts for steering chatbot behavior.
- How deep it goes
- Describes tone and length constraints but does not detail a formal output schema.
- Examples
- Uses the instruction to reply in a friendly, concise manner.
- What you can do after
- Configure response tone and length through prompt instructions.
Read how Building Reliable AI Systems explains output format →
Related concepts
- structural elementkind of · A structural element is one of the organized parts of a prompt, including its instruction, constraint, context, input parameter, output format, or delimiter.
- name-value pairuses · A name-value pair is a data association in which an identifying name or key corresponds to a value.
- constraintcontrasts with · A constraint is a condition or rule that restricts the permissible states, values, actions, or solutions in a system or problem.
- promptpart of · An input, typically containing instructions or context, supplied to an artificial-intelligence model to guide or elicit its output.
- JSONuses · JSON is a standardized, language-independent text format for representing structured data using objects, arrays, strings, numbers, Boolean values, and null.
- schemauses · A data schema is a formal specification of the structure, fields, types, relationships, and constraints of data.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.