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 # LlmEvalRuby
 
-TODO: Delete this and the text below, and describe your gem
-
-Welcome to your new gem! In this directory, you'll find the files you need to be able to package up your Ruby library into a gem. Put your Ruby code in the file `lib/llm_eval_ruby`. To experiment with that code, run `bin/console` for an interactive prompt.
+A Ruby gem that provides LLM evaluation functionality with prompt management and tracing capabilities. It supports multiple adapters for both local development and production environments with Langfuse integration.
 
 ## Installation
 
-TODO: Replace `UPDATE_WITH_YOUR_GEM_NAME_IMMEDIATELY_AFTER_RELEASE_TO_RUBYGEMS_ORG` with your gem name right after releasing it to RubyGems.org. Please do not do it earlier due to security reasons. Alternatively, replace this section with instructions to install your gem from git if you don't plan to release to RubyGems.org.
+Add this line to your application's Gemfile:
+
+```ruby
+gem 'llm_eval_ruby', git: 'https://github.com/test-IO/llm_eval_ruby'
+```
 
-Install the gem and add to the application's Gemfile by executing:
+And then execute:
 
-    $ bundle add UPDATE_WITH_YOUR_GEM_NAME_IMMEDIATELY_AFTER_RELEASE_TO_RUBYGEMS_ORG
+    $ bundle install
 
-If bundler is not being used to manage dependencies, install the gem by executing:
+## Configuration
 
-    $ gem install UPDATE_WITH_YOUR_GEM_NAME_IMMEDIATELY_AFTER_RELEASE_TO_RUBYGEMS_ORG
+Create an initializer file (e.g., `config/initializers/llm_eval_ruby.rb`):
+
+```ruby
+LlmEvalRuby.configure do |config|
+  # Choose adapter: :langfuse or :local
+  config.adapter = :langfuse
+  
+  # Langfuse configuration (for production)
+  config.langfuse_options = {
+    host: ENV['LANGFUSE_HOST'],
+    username: ENV['LANGFUSE_USERNAME'],
+    password: ENV['LANGFUSE_PASSWORD']
+  }
+  
+  # Local configuration (for development)
+  config.local_options = {
+    prompts_path: Rails.root.join('lib', 'prompts'),
+    traces_path: Rails.root.join('log', 'trace.log')
+  }
+end
+```
 
 ## Usage
 
-TODO: Write usage instructions here
+### 1. Basic Tracing
+
+```ruby
+# Create a trace for a job or operation
+trace = LlmEvalRuby::Tracer.trace(
+  name: 'test_case_generation',
+  session_id: 'session_123',
+  input: { url: 'https://example.com' },
+  user_id: 'test-generator',
+  metadata: { workflow_id: 'wf_123' }
+)
+
+# Create spans within a trace
+LlmEvalRuby::Tracer.span(name: :fetch_prompts, trace_id: trace.id) do
+  # Your code here
+end
+
+# Track AI generations
+generation = LlmEvalRuby::Tracer.generation(
+  name: 'generate_test_cases',
+  input: 'Generate test cases for login functionality',
+  trace_id: trace.id
+)
+
+# ... make AI API call ...
+
+generation.end(output: ai_response, usage: { tokens: 150 })
+```
+
+### 2. Observable Pattern for Services
+
+```ruby
+class OpenaiService
+  include LlmEvalRuby::Observable
+  
+  attr_reader :trace_id
+  
+  # Automatically create spans for these methods
+  observe :create_assistant, type: :span
+  observe :create_file, type: :span
+  observe :add_message, type: :span
+  
+  # Automatically track generation for this method
+  observe :chat, type: :generation
+  
+  def initialize(session_id, trace_id = nil)
+    @session_id = session_id
+    @trace_id = trace_id
+  end
+  
+  def create_assistant(params)
+    # Method implementation
+    # Automatically wrapped in a span
+  end
+  
+  def chat(params)
+    # Method implementation
+    # Automatically tracked as a generation
+  end
+end
+```
+
+### 3. Prompt Management
+
+#### Chat Prompts (System + User)
+```ruby
+# Fetch chat prompts (returns [system_prompt, user_prompt])
+system_prompt, user_prompt = LlmEvalRuby::PromptRepositories::Chat.fetch(
+  name: :validate_test_case
+)
+
+# Compile prompts with variables
+compiled_prompt = user_prompt.compile(
+  variables: {
+    test_case_content: "Login with valid credentials",
+    out_of_scope: "Performance testing"
+  }
+)
+
+# Use in AI call
+response = openai.chat(
+  parameters: {
+    model: 'gpt-4',
+    messages: [
+      { role: 'system', content: system_prompt.content },
+      { role: 'user', content: compiled_prompt.content }
+    ]
+  }
+)
+```
+
+#### Text Prompts (Single prompt)
+```ruby
+# Fetch and compile in one step
+prompt = LlmEvalRuby::PromptRepositories::Text.fetch_and_compile(
+  name: :precook_out_of_scope,
+  variables: {
+    feature_description: "User authentication",
+    out_of_scope: "Load testing, Security scanning"
+  }
+)
+
+# Use the compiled content
+response = openai.chat(
+  parameters: {
+    model: 'gpt-4',
+    messages: [{ role: 'user', content: prompt.content }]
+  }
+)
+```
+
+### 4. Real-World Example: Background Job
+
+```ruby
+class ValidateTestCaseJob < ApplicationJob
+  def perform(test_case_id)
+    @test_case = TestCase.find(test_case_id)
+    
+    # Start trace
+    @trace_id = LlmEvalRuby::Tracer.trace(
+      name: 'validate_test_case',
+      session_id: @test_case.session_id,
+      input: { test_case_id: test_case_id },
+      user_id: 'validator'
+    ).id
+    
+    # Fetch prompts with span tracking
+    LlmEvalRuby::Tracer.span(name: :fetch_prompts, trace_id: @trace_id) do
+      @system_prompt, @user_prompt = LlmEvalRuby::PromptRepositories::Chat.fetch(
+        name: :validate_test_case
+      )
+    end
+    
+    # Compile prompt with variables
+    compiled_prompt = @user_prompt.compile(
+      variables: {
+        test_case_content: @test_case.content,
+        requirements: @test_case.requirements
+      }
+    )
+    
+    # Track AI generation
+    generation = LlmEvalRuby::Tracer.generation(
+      name: 'validate_test_case',
+      input: compiled_prompt.content,
+      trace_id: @trace_id
+    )
+    
+    # Make AI call
+    response = openai_service.chat(
+      parameters: {
+        model: 'gpt-4',
+        messages: [
+          { role: 'system', content: @system_prompt.content },
+          { role: 'user', content: compiled_prompt.content }
+        ]
+      }
+    )
+    
+    # End generation tracking
+    generation.end(
+      output: response.dig('choices', 0, 'message', 'content'),
+      usage: response['usage']
+    )
+    
+    # Process response...
+  end
+  
+  private
+  
+  def openai_service
+    @openai_service ||= OpenaiService.new(@test_case.session_id, @trace_id)
+  end
+end
+```
+
+### 5. Prompt File Structure
+
+For local adapter, organize prompts in your `lib/prompts` directory:
+
+```
+lib/prompts/
+├── chat/
+│   ├── validate_test_case/
+│   │   ├── system.liquid
+│   │   └── user.liquid
+│   └── generate_test_cases/
+│       ├── system.liquid
+│       └── user.liquid
+└── text/
+    └── precook_out_of_scope.liquid
+```
+
+Example prompt file (`lib/prompts/chat/validate_test_case/user.liquid`):
+```liquid
+Please validate the following test case:
+
+Test Case Content:
+{{ test_case_content }}
+
+Out of Scope Items:
+{{ out_of_scope }}
+
+Determine if this test case is valid and in scope.
+```
 
 ## Development