Prompting - Miscellaneous
Ask LLM to rewrite instructions
Cookbook
Instructor - Basics
- Basic use
- Basic use via mixin
- Handling errors with `Maybe` helper class
- Modes
- Making some fields optional
- Private vs public object field
- Automatic correction based on validation results
- Using attributes
- Using LLM API connections from config file
- Validation
- Custom validation using Symfony Validator
- Validation across multiple fields
- Validation with LLM
Instructor - Advanced
- Context caching (structured output)
- Customize parameters of LLM driver
- Custom prompts
- Using structured data as an input
- Extracting arguments of function or method
- Streaming partial updates during inference
- Providing example inputs and outputs
- Extracting scalar values
- Extracting sequences of objects
- Streaming
- Structures
Instructor - Troubleshooting
Instructor - LLM API Support
Instructor - Extras
- Extraction of complex objects
- Extraction of complex objects (Anthropic)
- Extraction of complex objects (Cohere)
- Extraction of complex objects (Gemini)
- Image processing - car damage detection
- Image to data (OpenAI)
- Image to data (Anthropic)
- Image to data (Gemini)
- Generating JSON Schema from PHP classes
- Generating JSON Schema dynamically
- Create tasks from meeting transcription
- Translating UI text fields
- Web page to PHP objects
Polyglot - LLM Basics
Polyglot - LLM Advanced
Polyglot - LLM Troubleshooting
Polyglot - LLM API Support
Polyglot - LLM Extras
Prompting - Zero-Shot Prompting
Prompting - Few-Shot Prompting
Prompting - Thought Generation
Prompting - Miscellaneous
- Arbitrary properties
- Consistent values of arbitrary properties
- Chain of Summaries
- Chain of Thought
- Single label classification
- Multiclass classification
- Entity relationship extraction
- Handling errors
- Limiting the length of lists
- Reflection Prompting
- Restating instructions
- Ask LLM to rewrite instructions
- Expanding search queries
- Summary with Keywords
- Reusing components
- Using CoT to improve interpretation of component data
Prompting - Miscellaneous
Ask LLM to rewrite instructions
Overview
Asking LLM to rewrite the instructions and rules is another way to improve inference results.
You can provide arbitrary instructions on the data handling in the class and property PHPDocs. Instructor will use these instructions to guide LLM in the inference process.
Example
<?php
require 'examples/boot.php';
use Cognesy\Instructor\Instructor;
/**
* Identify what kind of job the user is doing.
* Typical roles we're working with are CEO, CTO, CFO, CMO.
* Sometimes user does not state their role directly - you will need
* to make a guess, based on their description.
*/
class UserRole
{
/**
* Rewrite the instructions and rules in a concise form to correctly
* determine the user's title - just the essence.
*/
public string $instructions;
/** Role description */
public string $description;
/** Most likely job title */
public string $title;
}
class UserDetail
{
public string $name;
public int $age;
public UserRole $role;
}
$text = <<<TEXT
I'm Jason, I'm 28 yo. I am responsible for driving growth of our
company.
TEXT;
$instructor = new Instructor;
$user = $instructor->respond(
messages: [["role" => "user", "content" => $text]],
responseModel: UserDetail::class,
);
dump($user);
assert($user->name === "Jason");
assert($user->age === 28);
assert(!empty($user->role->title));
?>