There is no universally best serialization format for LLM inputs. Choose according to the boundary you are working at: prompt context, structured model output, tool arguments, or application storage and transport. For predictable output fields, use the model provider’s constrained JSON or schema feature when available; for prompt context, prioritize readable, unambiguous boundaries; for application records, use a format such as Protocol Buffers when its typed, cross-language serialization benefits matter.
Start by identifying what you are serializing
“LLM input format” can mean several different things. The right choice changes depending on whether you are sending context to a model, asking it to return fields for software, invoking a tool, or storing and moving records between application components.
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- Prompt context: information the model reads, such as user-provided text or retrieved documents.
- Structured response: data the model returns for downstream code to consume.
- Tool arguments: parameters used to call a function or external tool.
- Application storage or transport: records exchanged between software services, possibly before they are rendered for the model.
Before picking JSON, YAML, XML, or a binary format, check whether the model API natively supports or constrains the representation you need. Also consider schema validation, human readability, treatment of arbitrary text, measured token and latency costs, application portability and version evolution, and interoperability.
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Choose a format for the job
| Use case | Practical choice | Why | Important qualification |
|---|---|---|---|
| Model response must match fields expected by software | Provider-supported constrained JSON or schema output | It is designed to make responses conform to a declared structure. | Check support for your model and the provider’s schema subset, as well as refusal and failure behavior. Valid JSON alone does not guarantee schema adherence. |
| Calling a function, tool, or external data source | Provider-supported tool or function calling | Tool invocation is a distinct API use case from simply formatting a response as structured data. | Follow the provider’s current tool interface and validation requirements. |
| Simple prompt context | Plain text with clear labels, or a structured representation if it clarifies boundaries | Readable labels can be enough when the context is simple. | For rich or untrusted content, clearly separate instructions from data and state how the model should treat that data. |
| Typed records moving through an application | Protocol Buffers (Protobuf), where its application-level features fit | Protobuf supports typed structured data, compact storage, generated language bindings, and extensibility. | Its binary wire representation is not automatically a useful prompt format; render or convert data into a representation supported by the model interface. |
| Provider-specific conversation stream | That provider’s documented native interface | A native format may encode message structure and metadata expected by the model. | OpenAI’s Harmony, for example, is a model interface format—not a general recommendation to hand-author provider-specific streams. |
| Connecting an AI application to external tools or data | A suitable integration protocol, such as MCP | Connectivity protocols address how applications connect to tools and data sources. | MCP is not a universal encoding for prompt content. |
When model output needs a schema
If downstream code depends on known fields and types, prefer a provider’s schema-constrained output feature when the target model supports the required schema. OpenAI distinguishes Structured Outputs from JSON mode: both can produce valid JSON, but JSON mode does not ensure that the result follows a particular schema. OpenAI recommends function calling when connecting the model to tools, functions, or data, and a structured response format when the model’s response itself needs structure. See the OpenAI Structured Outputs guide.
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Anthropic also documents schema-constrained JSON outputs and strict tool use as separate features that can be combined. Check the current supported schema subset and handling of refusals or failures for the model and API you use; do not treat a successful text response as proof that your application contract was met. Anthropic’s current details are in its structured outputs documentation.
When JSON, YAML, or XML is prompt context
For information embedded in a prompt, choose the representation that makes the boundary between instructions and data clearest to both people and the model. Plain text and labels may be sufficient for simple context. For larger or untrusted passages, use explicit delimiters or structure, and say that embedded content is data rather than instructions to follow.
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OpenAI’s Model Spec advises using an untrusted_text block where available; otherwise it suggests YAML, JSON, or XML according to readability and escaping. JSON and XML require escaping, while YAML relies on indentation. Those trade-offs affect how easy a prompt is to read and maintain, but syntax by itself is not a security guarantee and does not block prompt injection. See the OpenAI Model Spec.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsFor example, a prompt can label an untrusted passage explicitly and instruct the model to summarize it as content, not obey requests contained inside it. A JSON or XML wrapper may make the boundary visible, but it does not replace careful instruction design or application-side safeguards.
When Protobuf belongs in the system
Protobuf is an application serialization format for typed structured data. Google highlights compact storage, fast parsing, generated code, and extensibility among its characteristics. Those benefits can matter when services need language bindings, typed records, or a format designed to evolve. See Google’s Protocol Buffers overview.
That does not mean the model should receive Protobuf’s binary wire representation. Unless the endpoint explicitly accepts it, convert the application record into a model-readable text or multimodal representation at the model boundary. Keep the storage and transport decision separate from the prompt-format decision.
Evaluate candidates on your actual workload
Official documentation describes API and serialization capabilities, but it does not establish a universal ranking of JSON, YAML, XML, or other formats for token efficiency or task accuracy. Do not assume a format saves tokens or improves answers without measuring it on the target model and task.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →- Define the boundary. Decide whether the format is for prompt context, a structured response, tool arguments, or application storage and transport.
- Check native support. Review the current provider documentation for supported models, schema limits, tool interfaces, and refusal or failure behavior.
- Set the contract. Specify the fields, types, required values, and what your application should do if the model refuses or returns an unusable result.
- Prepare representative inputs. Include ordinary cases, long or irregular data, and untrusted text if those occur in production.
- Compare in practice. Record task success, malformed or schema-invalid results, token usage, latency, and how easily people can debug the prompts.
- Keep application serialization behind the boundary. Convert typed storage records into a model-supported representation where needed rather than assuming the storage encoding is prompt-ready.
Common format-selection mistakes
- Equating valid JSON with a valid contract: syntax validity does not establish that required fields and types are present. Use supported schema-constrained output when adherence matters, then handle failures in application code.
- Using structured response formatting for tool execution: a formatted answer and a tool call serve different purposes. Choose the API feature that matches the action.
- Assuming markup prevents prompt injection: labels and delimiters communicate boundaries; they are not a security barrier.
- Choosing by presumed token savings: compare token use and latency on representative requests instead of relying on a universal JSON-versus-YAML-versus-XML claim.
- Sending an application wire format directly to the model: a compact binary transport can be useful between services while still requiring conversion at the model interface.
- Hand-authoring a provider’s internal conversation format without a need: follow the documented interface and avoid tying an otherwise portable application to provider-specific serialization unnecessarily.
Sources and scope
The provider and protocol documentation cited here was accessed on October 4, 2026. API capabilities and supported schema subsets can change, so verify the current documentation for the exact model and endpoint you deploy. The cited sources describe capabilities and design guidance; they do not provide a controlled, universal comparison of format accuracy or token efficiency.
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