Automatic LLM prompt optimization Ruby

```ruby
# Automatic LLM Prompt Optimization Example in Ruby

# This example demonstrates a very basic simulation of automatic prompt optimization.
# In reality, this is a complex process involving machine learning models.
# This simplified version shows the core idea: iterating and refining a prompt.

# Define a sample LLM function (in reality, this would be a call to a real LLM API)
def llm_response(prompt)
  # This is a VERY basic mock LLM response.  In reality, this would involve calling
  # an API like OpenAI's GPT-3/4.  This provides a predictable output for demonstration.

  case prompt
  when "Write a short story."
    "A cat sat on a mat."
  when "Write a short story. Be more descriptive."
    "A fluffy, ginger cat sat comfortably on a worn, woven mat."
  when "Write a short, captivating story. Be more descriptive and add a sense of mystery."
    "A fluffy, ginger cat sat comfortably on a worn, woven mat.  But the house was deathly silent, the air thick with secrets."
  else
    "I don't understand."
  end
end


# Initial prompt
prompt = "Write a short story."
puts "Initial Prompt: #{prompt}"
response = llm_response(prompt)
puts "Initial Response: #{response}"

# Define a simple evaluation function (in reality, this would be more sophisticated)
def evaluate_response(response)
  # This is a VERY simple evaluation.  In a real system, you'd use metrics like
  # length, complexity, relevance, etc.  This just checks length.
  response.length > 20 # "Good" if the response is longer than 20 characters.
end

# Optimization loop
puts "\nStarting Optimization..."
iterations = 3
iterations.times do |i|
  puts "\nIteration #{i+1}:"

  # Evaluate the current response
  is_good = evaluate_response(response)
  puts "Response quality: #{is_good ? 'Good' : 'Not Good'}"

  # Based on the evaluation, refine the prompt
  if !is_good
    case i
    when 0
      prompt = "Write a short story. Be more descriptive."
    when 1
      prompt = "Write a short, captivating story. Be more descriptive and add a sense of mystery."
    else
      prompt = "Write a short, captivating, engaging story. Be more descriptive, add a sense of mystery and drama."
    end


    puts "Refined Prompt: #{prompt}"
    response = llm_response(prompt)
    puts "Response: #{response}"
  else
    puts "Prompt is good enough. Stopping optimization."
    break # Stop if the response is considered good enough
  end
end

puts "\nFinal Optimized Prompt: #{prompt}"
puts "Final Response: #{response}"


# Explanation:

# 1. `llm_response(prompt)`:
#    - This function *simulates* an LLM.  In a real application, this would be
#      replaced with a call to an LLM API (e.g., using the `openai` gem).
#    - It takes a `prompt` as input and returns a response string.  The response
#      is very basic and hardcoded for this example.

# 2. `evaluate_response(response)`:
#    - This function *simulates* evaluating the quality of the LLM's response.
#    - In a real application, this would involve more complex logic to assess
#      factors like relevance, coherence, factual accuracy, creativity, etc.  You
#      might use other LLMs to evaluate the response or even human evaluators.
#    - In this example, it simply checks if the response is longer than 20 characters.

# 3. Optimization Loop:
#    - The `iterations` variable controls how many times the prompt is refined.
#    - Inside the loop:
#      - The current `response` is evaluated using `evaluate_response`.
#      - If the response is *not* considered good, the `prompt` is modified.
#        The modification is done using a `case` statement.  In a real system,
#        this would involve more sophisticated prompt engineering techniques or
#        even machine learning models to predict the optimal prompt changes.
#      - The `llm_response` function is called again with the refined `prompt`
#        to get a new response.
#      - If the response *is* considered good, the loop breaks, indicating
#        that the optimization process is complete.

# Key Concepts:

# - **Prompt Engineering:**  The process of crafting effective prompts to guide
#   an LLM to produce the desired output.
# - **Evaluation Metrics:**  Metrics used to assess the quality of the LLM's
#   response (e.g., relevance, coherence, factual accuracy).
# - **Iteration:**  The process of repeatedly refining the prompt and evaluating
#   the response until a satisfactory result is achieved.
# - **Automation:**  The goal of automatic prompt optimization is to automate
#   the iterative process of prompt engineering using machine learning models
#   or other techniques.

# Limitations of this Example:

# - **Simplified LLM:** The `llm_response` function is a mock and doesn't
#   represent a real LLM's capabilities.
# - **Basic Evaluation:** The `evaluate_response` function is very basic and
#   doesn't capture the complexity of evaluating LLM responses.
# - **Hardcoded Prompt Refinement:** The prompt refinement logic is hardcoded
#   and doesn't use any machine learning or sophisticated prompt engineering
#   techniques.

# This example is intended to provide a conceptual understanding of automatic
# prompt optimization.  Building a real-world system would require significantly
# more complex techniques and integration with actual LLM APIs.
```