Designing Effective Language Model Programs
===========================================

Introduction
------------

Creating effective Language Model Programs (LMPs) is crucial for leveraging the full power of large language models in your applications. This guide will cover best practices and strategies for designing LMPs that are efficient, maintainable, and produce high-quality results.

Principles of Effective LMP Design
----------------------------------

1. Single Responsibility
^^^^^^^^^^^^^^^^^^^^^^^^

Each LMP should have a single, well-defined purpose. This makes your programs easier to understand, test, and maintain.

Good example:

.. code-block:: python

    @ell.simple(model="gpt-4")
    def summarize_article(article: str) -> str:
        """Summarize the given article in three sentences."""
        return f"Please summarize this article in three sentences:\n\n{article}"

2. Clear and Concise Prompts
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Write clear, concise prompts that give the language model specific instructions.

Good example:

.. code-block:: python

    @ell.simple(model="gpt-4")
    def generate_product_name(description: str) -> str:
        """You are a creative marketing expert. Generate a catchy product name."""
        return f"Create a catchy, memorable name for a product with this description: {description}. The name should be no more than 3 words long."

3. Leverage System Messages
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Use the system message (function docstring in ``@ell.simple``) to set the context and role for the language model.

Good example:

.. code-block:: python

    @ell.simple(model="gpt-4")
    def code_reviewer(code: str) -> str:
        """You are an experienced software engineer conducting a code review. Your feedback should be constructive, specific, and actionable."""
        return f"Review the following code and provide feedback:\n\n```python\n{code}\n```"

4. Use Strong Typing
^^^^^^^^^^^^^^^^^^^^

Leverage Python's type hints to make your LMPs more robust and self-documenting.

Good example:

.. code-block:: python

    from typing import List

    @ell.simple(model="gpt-4")
    def categorize_items(items: List[str]) -> List[str]:
        """Categorize each item in the list."""
        items_str = "\n".join(items)
        return f"Categorize each of the following items into one of these categories: Food, Clothing, Electronics, or Other.\n\n{items_str}"

5. Modular Design
^^^^^^^^^^^^^^^^^

Break complex tasks into smaller, composable LMPs.

Good example:

.. code-block:: python

    @ell.simple(model="gpt-4")
    def extract_key_points(text: str) -> str:
        """Extract the key points from the given text."""
        return f"Extract the 3-5 most important points from this text:\n\n{text}"

    @ell.simple(model="gpt-4")
    def generate_summary(key_points: str) -> str:
        """Generate a summary based on key points."""
        return f"Create a coherent summary paragraph using these key points:\n\n{key_points}"

    def summarize_long_text(text: str) -> str:
        points = extract_key_points(text)
        return generate_summary(points)

6. Error Handling
^^^^^^^^^^^^^^^^^

Design your LMPs to handle potential errors gracefully.

Good example:

.. code-block:: python

    @ell.simple(model="gpt-4")
    def answer_question(question: str) -> str:
        """You are a helpful AI assistant answering user questions."""
        return f"""
        Answer the following question. If you're not sure about the answer, say "I'm not sure" and explain why:

        Question: {question}
        """

7. Consistent Formatting
^^^^^^^^^^^^^^^^^^^^^^^^

Maintain consistent formatting in your prompts for better readability and maintainability.

Good example:

.. code-block:: python

    @ell.simple(model="gpt-4")
    def analyze_sentiment(text: str) -> str:
        """Analyze the sentiment of the given text."""
        return f"""
        Analyze the sentiment of the following text. Respond with one of these options:
        - Positive
        - Neutral
        - Negative

        Then provide a brief explanation for your choice.

        Text: {text}
        """

Advanced Techniques
-------------------

1. Few-Shot Learning
^^^^^^^^^^^^^^^^^^^^

Provide examples in your prompts to guide the model's responses.

Example:

.. code-block:: python

    @ell.simple(model="gpt-4")
    def generate_poem(topic: str) -> str:
        """You are a skilled poet. Generate a short poem on the given topic."""
        return f"""
        Write a short, four-line poem about {topic}. Here's an example format:

        Topic: Sun
        Poem:
        Golden orb in azure sky,
        Warming earth as day goes by,
        Life-giving light, nature's friend,
        Day's journey to night's soft end.

        Now, create a poem about: {topic}
        """

2. Chain of Thought Prompting
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Encourage the model to show its reasoning process.

Example:

.. code-block:: python

    @ell.simple(model="gpt-4")
    def solve_math_problem(problem: str) -> str:
        """You are a math tutor helping a student solve a problem."""
        return f"""
        Solve the following math problem. Show your work step-by-step, explaining each step clearly.

        Problem: {problem}

        Solution:
        1) [First step]
        2) [Second step]
        ...
        
        Final Answer: [Your answer here]
        """

3. Iterative Refinement
^^^^^^^^^^^^^^^^^^^^^^^

Use multiple LMPs in sequence to refine outputs.

Example:

.. code-block:: python

    @ell.simple(model="gpt-4")
    def draft_essay(topic: str) -> str:
        """Create a first draft of an essay on the given topic."""
        return f"Write a first draft of a short essay about {topic}."

    @ell.simple(model="gpt-4")
    def improve_essay(essay: str) -> str:
        """Improve the given essay draft."""
        return f"""
        Improve the following essay draft. Focus on:
        1. Clarifying main points
        2. Improving transitions between paragraphs
        3. Enhancing the conclusion

        Essay draft:
        {essay}
        """

    def create_polished_essay(topic: str) -> str:
        first_draft = draft_essay(topic)
        return improve_essay(first_draft)

Best Practices for Complex LMPs
-------------------------------

When working with ``@ell.complex`` and multi-turn conversations:

1. **Maintain Context**: Ensure that relevant information is carried through the conversation.

2. **Use Tools Judiciously**: When integrating tools, provide clear instructions on when and how to use them.

3. **Handle Ambiguity**: Design your LMPs to ask for clarification when inputs are ambiguous.

4. **Stateful Interactions**: For stateful LMPs, clearly define what information should be maintained between turns.

Example of a complex LMP:

.. code-block:: python

    @ell.complex(model="gpt-4", tools=[get_weather, search_database])
    def travel_assistant(message_history: List[ell.Message]) -> List[ell.Message]:
        return [
            ell.system("""
            You are a travel assistant helping users plan their trips. 
            Use the get_weather tool to check weather conditions and the search_database tool to find information about destinations.
            Always confirm the user's preferences before making recommendations.
            """),
            *message_history
        ]

Conclusion
----------

Designing effective Language Model Programs is both an art and a science. By following these principles and techniques, you can create LMPs that are more efficient, maintainable, and produce higher quality results. Remember to iterate on your designs, test thoroughly, and always consider the end-user experience when crafting your prompts.