Customize Prompts

Instructor builds a structured prompt from several components: system text, user messages, a mode-specific instruction prompt, examples, and retry context. You can customize most of these to tune extraction behavior without changing the underlying extraction flow.

The default StructuredPromptRequestMaterializer uses prompt classes backed by bundled Twig templates. Customize the default path by supplying your own prompt classes.

RequestMaterializer and its inline prompt/chat-structure settings are deprecated 2.5 compatibility APIs. They have no effect on the default path and work only when that legacy materializer is injected explicitly.

System And Prompt Text

The two most common customization points are the system message and the prompt text:

use Cognesy\Instructor\StructuredOutput;

$result = (new StructuredOutput)
    ->withSystem('You are a precise data extraction assistant. Return only factual data.')
    ->withPrompt('Extract the contact details from the text below.')
    ->with(messages: $text, responseModel: Contact::class)
    ->get();

• System text sets the model's persona and overall behavior. Use it for stable instructions that apply across many requests.
• Prompt text provides task-specific instructions for this particular extraction. On the default structured prompt path it is rendered inside the single system prompt body alongside the mode-specific extraction instructions.

You can also pass both through the with() method:

$result = (new StructuredOutput)
    ->with(
        messages: $text,
        responseModel: Contact::class,
        system: 'Return concise, accurate data.',
        prompt: 'Extract the contact details.',
    )
    ->get();

Examples

Few-shot examples are another prompt component. On the default structured prompt path they are rendered as markdown inside the system prompt to demonstrate the expected extraction style:

use Cognesy\Instructor\Extras\Example\Example;

$result = (new StructuredOutput)
    ->withExamples([
        Example::fromText('Jane Doe, 31', ['name' => 'Jane Doe', 'age' => 31]),
    ])
    ->with(messages: $text, responseModel: Person::class)
    ->get();

See the Demonstrations page for details on the Example class.

Cached Context

Some providers (notably Anthropic) support prompt caching, where stable parts of the conversation are cached between requests to reduce latency and cost. Use withCachedContext() to mark content as cacheable:

$result = (new StructuredOutput)
    ->withCachedContext(
        messages: $referenceDocument,
        system: 'You are a document analyst.',
        prompt: 'Extract entities from the document.',
        examples: $examples,
    )
    ->with(messages: 'Now extract from this specific paragraph...', responseModel: Entity::class)
    ->get();

The cached context is placed before the per-request content in the prompt. On the new structured prompt path, cached system text, cached task text, and cached examples are rendered into a cached system prompt and projected through provider-native cached context. Content passed through withCachedContext() is marked with cache control headers where the provider supports them.

Mode-Specific Prompts

Instructor uses a default prompt class for each output mode that tells the model how to format its response. The bundled classes use .md.twig templates and are configured in StructuredOutputConfig.

Mode	Default prompt behavior
Tools	"Extract correct and accurate data from the input using provided tools."
Json	Includes the JSON Schema and asks for a strict JSON response
JsonSchema	Asks for a strict JSON response following the provided schema
MdJson	Includes the JSON Schema and asks for JSON inside a Markdown code block

Overriding Mode Prompts

Use prompt classes as the supported customization seam:

$config = new StructuredOutputConfig(
    modePromptClasses: [
        OutputMode::Tools->value => App\Prompts\ToolsSystemPrompt::class,
        OutputMode::Json->value => App\Prompts\JsonSystemPrompt::class,
        OutputMode::JsonSchema->value => App\Prompts\JsonSchemaSystemPrompt::class,
        OutputMode::MdJson->value => App\Prompts\MdJsonSystemPrompt::class,
    ],
    retryPromptClass: App\Prompts\RetryFeedbackPrompt::class,
    deserializationErrorPromptClass: App\Prompts\DeserializationRepairPrompt::class,
);

If you store these in YAML, use FQN strings:

modePromptClasses:
  tool_call: 'App\\Prompts\\ToolsSystemPrompt'
  json: 'App\\Prompts\\JsonSystemPrompt'
  json_schema: 'App\\Prompts\\JsonSchemaSystemPrompt'
  md_json: 'App\\Prompts\\MdJsonSystemPrompt'

retryPromptClass: 'App\\Prompts\\RetryFeedbackPrompt'
deserializationErrorPromptClass: 'App\\Prompts\\DeserializationRepairPrompt'

Tool Name And Description

In OutputMode::Tools, the tool definition sent to the model includes a name and description. These provide semantic context that can improve extraction quality:

use Cognesy\Instructor\Config\StructuredOutputConfig;

$config = new StructuredOutputConfig(
    toolName: 'extract_person',
    toolDescription: 'Extract personal information from the provided text.',
);

The defaults are extracted_data and Function call based on user instructions. respectively. Overriding them with task-specific values can help the model understand what the tool represents.

OutputMode::Json and OutputMode::MdJson ignore tool name and description since they do not use tool calling.

Retry Prompt

When validation fails and retries are enabled, Instructor renders the configured retry prompt class:

$config = new StructuredOutputConfig(
    retryPromptClass: App\Prompts\RetryFeedbackPrompt::class,
);

The same pattern applies to deserialization repair via deserializationErrorPromptClass.

Legacy Compatibility

The inline modePrompts, retryPrompt, and chatStructure settings are retained only for applications that explicitly inject the deprecated RequestMaterializer. They are scheduled for removal in 2.6. New code should use prompt classes or provide a custom CanMaterializeRequest implementation when prompt-class customization is insufficient.