Category: LLM

LLM Prompt Optimizations: Practical Techniques for Developers

Optimizing inputs for LLMs ensures better, more consistent outputs while leveraging the full potential of the model’s underlying capabilities. By understanding core concepts like tokenization, embeddings, self-attention, and context limits, you can tailor inputs to achieve desired outcomes reliably. Below, you’ll find fundamental techniques and best practices organized into practical strategies.

PS: You can use this content to automatically improve your prompt by asking as follows: https://chatgpt.com/share/6785a41d-72a0-8002-a1fe-52c14a5fb1e5

🎯 1. Controlling Probabilities: Guide Model Outputs

🧠 Theory: LLMs always follow probabilities when generating text. For every token, the model calculates a probability distribution based on the context provided. By carefully structuring inputs or presenting examples, we can shift the probabilities toward the desired outcome:

  • Providing more examples helps the model identify patterns and generate similar outputs.
  • Clear instructions reduce ambiguity, increasing the probability of generating focused responses.
  • Contextual clues and specific phrasing subtly guide the model to prioritize certain outputs.

⚙️ Technology: The model operates using token probabilities:

  • Each token (word or part of a word) is assigned a likelihood based on the input context.
  • By influencing the input, we can make certain tokens more likely to appear in the output.

For example:

  • A general query like “Explain energy sources” might distribute probabilities evenly across different energy types.
  • A more specific query like “Explain why solar energy is sustainable” shifts the probabilities toward solar-related tokens.

⚙️ Shifting Probabilities in Prompts: The structure and wording of your prompt significantly influence the token probabilities:

  • For specific outputs: Use targeted phrasing to increase the likelihood of desired responses: Explain why renewable energy reduces greenhouse gas emissions.
  • For diverse outputs: Frame open-ended questions to distribute probabilities across a broader range of topics: What are the different ways to generate clean energy?
  • Few-Shot Learning: Guide the model using few-shot learning to set patterns: Example 1: Input: Solar energy converts sunlight into electricity. Output: Solar energy is a renewable power source. Example 2: Input: Wind energy generates power using turbines. Output: Wind energy is clean and sustainable. Task: Input: Hydropower generates electricity from flowing water. Output:

💡 Prompt Tips:

  • Use clear, direct instructions for precise outputs: Write a PHP function that adds two integers and returns a structured response as an array.
  • Use contextual clues to steer the response: Explain why PHP is particularly suited for web development.

💻 Code Tips: LLMs break down code and comments into tokens, so structuring your PHPDocs helps focus probabilities effectively. Provide clarity and guidance through structured documentation:

/**
 * Adds two integers and returns a structured response.
 *
 * @param int $a The first number.
 * @param int $b The second number.
 * 
 * @return array{result: int, message: string} A structured response with the sum and a message.
 */
function addIntegers(int $a, int $b): array {
    $sum = $a + $b;

    return [
        'result' => $sum,
        'message' => "The sum of $a and $b is $sum."
    ];
}
  • Include examples in PHPDocs to further refine the probabilities of correct completions: /** * Example: * Input: addIntegers(3, 5) * Output: [‘result’ => 8, ‘message’ => ‘The sum of 3 and 5 is 8’] */

✂️ 2. Tokenization and Embeddings: Use Context Efficiently

🧠 Theory: LLMs break down words into tokens (numbers) to relate them to each other in multidimensional embeddings (vectors). The more meaningful context you provide, the better the model can interpret relationships and generate accurate outputs:

  • Tokens like “renewable energy” and “sustainability” have semantic proximity in the embedding space.
  • More context allows the model to generate richer and more coherent responses.

⚙️ Technology:

  • Tokens are the smallest units the model processes. For example, “solar” and “energy” may be separate tokens, or in compound languages like German, one long word might be broken into multiple tokens.
  • Embeddings map these tokens into vectors, enabling the model to identify their relationships in high-dimensional space.

⚙️ Optimizing Tokenization in Prompts: To make the most of tokenization and embeddings:

  • Minimize irrelevant tokens: Focus on core concepts and avoid verbose instructions.
  • Include context-rich phrases: Relevant terms improve the embedding connections.
  • Simplify Language: Use concise phrasing to minimize token count: Solar energy is renewable and reduces emissions.
  • Remove Redundancy: Eliminate repeated or unnecessary words: Explain why solar energy is sustainable.

💡 Prompt Tips:

  • Include only essential terms for better embedding proximity: Describe how solar panels generate electricity using photovoltaic cells.
  • Avoid vague or verbose phrasing: Explain solar energy and its uses in a way that a normal person can understand and provide details.
  • Use specific language to avoid diluting the context: Explain why solar energy is considered environmentally friendly and cost-effective.
  • Avoid vague instructions that lack actionable context: Explain me solar energy.

💻 Code Tips: Write compact and clear PHPDocs to save tokens and improve context:

/**
 * Converts raw user input into a structured format.
 *
 * @param string $input Raw input data.
 * 
 * @return array{key: int, value: string} Structured output.
 */
function parseInput(string $input): array {
    $parts = explode(":", $input);

    return [
        'key' => (int)$parts[0],
        'value' => trim($parts[1])
    ];
}
  • Use compact and descriptive documentation to maximize token efficiency: /** * Example: * Input: “42:Hello” * Output: [‘foo’ => 42, ‘bar’ => ‘Hello’] */

🧭 3. Self-Attention and Structure: Prioritize Context

🧠 Theory: LLMs work with the principle of self-attention, where the input tokens are interrelated with each other to determine the relevance and context. This mechanism assigns importance scores to tokens, ensuring that the most relevant words and their relationships are prioritized.

⚙️ Technology:

  • Self-attention layers: Compare each token with every other token in the input to generate an attention score.
  • Multi-head attention: Allows the model to consider multiple perspectives simultaneously, balancing relevance and context.
  • Pitfall: Too many irrelevant tokens dilute the attention scores, leading to distorted outputs.

⚙️ Optimizing Structure in Prompts:

  • Structure Your Inputs: Use lists, steps, or sections to emphasize relationships: Compare the benefits of solar and wind energy: 1. Environmental impact 2. Cost-efficiency 3. Scalability
  • Minimize Irrelevant Tokens: Keep prompts focused and free from extraneous details.

💡 Prompt Tips:

  • Well-Structured: Organize tasks into sections: Explain the environmental and economic benefits of renewable energy in two sections: 1. Environmental 2. Economic
  • Unstructured: Avoid asking everything at once: What are the environmental and economic benefits of renewable energy?

💻 Code Tips: In PHPDocs, organize information logically to enhance clarity and guide models effectively:

/**
 * Calculates the cost efficiency of renewable energy.
 *
 * Steps:
 * 1. Evaluate savings-to-investment ratio.
 * 2. Return a percentage efficiency score.
 *
 * @param float $investment Initial investment cost.
 * @param float $savings Annual savings.
 * 
 * @return float Efficiency percentage.
 */
function calculateEfficiency(float $investment, float $savings): float {
    return ($savings / $investment) * 100;
}

🧹 4. Context Management and Token Limits

🧠 Theory: LLMs operate within a fixed token limit (e.g., ~8k tokens for GPT-4), encompassing both input and output. Efficiently managing context ensures relevant information is prioritized while avoiding irrelevant or redundant content.

⚙️ Technology:

  • Chunking: Break long inputs into smaller, manageable parts: Step 1: Summarize the introduction of the report. Step 2: Extract key arguments from Section 1. Step 3: Combine summaries for a final overview.
  • Iterative Summarization: Condense sections before integrating them: Summarize Section 1: Solar energy’s benefits. Summarize Section 2: Wind energy’s benefits. Combine both summaries.
  • Pitfall: Excessive context can truncate critical data due to token limits.

💡 Prompt Tips:

  • For large inputs, use step-by-step processing: Step 1: Summarize the introduction of the document. Step 2: Extract key arguments from Section 1. Step 3: Combine these points into a cohesive summary.
  • Avoid presenting the full text in a single prompt: Summarize this 20-page document.
  • Focus on specific sections or tasks: Summarize the introduction and key points from Section 1.

💻 Code Tips: Divide tasks into smaller functions to handle token limits better:

function summarizeSection(string $section): string {
    // Summarize section content.
}

function combineSummaries(array $summaries): string {
    // Merge individual summaries.
}

🎨 5. Reasoning and Goals: Strengthen Prompt Direction

🧠 Theory: LLMs generate better results when the reasoning behind a task and its intended goal are explicitly stated. This guides the model’s probabilities toward meaningful and relevant outcomes.

⚙️ Technology:

  • Explicit reasoning provides semantic depth, helping the model focus on the task’s purpose.
  • Explaining the goal improves alignment with user expectations and narrows token probabilities.

💡 Prompt Tips:

  • State the reason for the task and its goal: Explain renewable energy because I need to create an introductory guide for high school students.
  • Avoid generic prompts without a clear goal: Describe renewable energy.

💻 Code Tips: Use PHPDocs to explain both the reasoning and expected outcomes of a function:

/**
 * Generates a detailed user profile report.
 *
 * This function is designed to create a comprehensive profile report based on user data inputs. 
 * It is useful for analytical dashboards requiring well-structured user insights.
 *
 * @param array{name: string, age: int, email: string} $userData The user data array.
 * 
 * @return string A formatted profile report.
 */
function generateProfileReport(array $userData): string {
    return sprintf(
        "User Profile:\nName: %s\nAge: %d\nEmail: %s\n",
        $userData['name'],
        $userData['age'],
        $userData['email']
    );
}

🛠️ 6. Iterative Refinement: Simplify Complex Tasks

🧠 Theory:
Breaking down complex tasks into smaller, manageable steps improves accuracy and ensures the model generates focused and coherent outputs. This method allows you to iteratively refine results, combining outputs from smaller subtasks into a complete solution.

⚙️ Technology:

  • Chunking: Split large tasks into multiple smaller ones to avoid overwhelming the model.
  • Validation: Intermediate outputs can be validated before moving to the next step, minimizing errors.
  • Recombination: Smaller validated outputs are merged for the final result.

💡 Prompt Tips:

  • For multi-step tasks, provide clear, incremental instructions: Step 1: Summarize the environmental benefits of solar energy. Step 2: Describe the cost savings associated with solar energy. Step 3: Combine these summaries into a single paragraph.
  • Avoid handling complex tasks in a single step: Explain the environmental benefits and cost savings of solar energy in one response.

💻 Code Tips: Ask the LLM to create the code step by step and ask for confirmation after each step so that the LLM can focus on one aspect of the implementation at a time. Focus on one aspect of the implementation at a time.

🔗 7. Cross-Contextual Coherence: Maintain Consistency

🧠 Theory:
LLMs lack persistent memory between interactions, making it essential to reintroduce necessary context for consistent responses across prompts. By maintaining cross-contextual coherence, outputs remain aligned and relevant, even in multi-step interactions.

⚙️ Technology:

  • Use context bridging: Reference key elements from previous responses to maintain relevance.
  • Store critical details in persistent structures, such as arrays or JSON, to reintroduce when needed.
  • Avoid overloading with irrelevant details, which can dilute coherence.

💡 Prompt Tips:

  • Reintroduce essential context from previous interactions: Based on our discussion about renewable energy, specifically solar power, explain the benefits of wind energy.
  • Summarize intermediate outputs for clarity: Summarize the main benefits of renewable energy. Then expand on solar and wind energy.

💻 Code Tips: Use seperated files for Code-Examples that we can provide e.g. Custom GPTs so it can learn from learnings/findings this way.

🌍 8. Style and Tone: Adapt Outputs to the Audience

🧠 Theory: LLMs generate better responses when the desired style and tone are explicitly stated. By matching the tone to the audience, you can make content more engaging and effective.

⚙️ Technology:

  • The model uses semantic cues in the prompt to adjust style and tone.
  • Specific words and phrases like “formal,” “casual,” or “technical” help steer the model’s output.

💡 Prompt Tips:

  • Specify the tone and audience: Write a technical explanation of solar panels for an engineering audience.
  • Adjust the style for different contexts: Explain solar panels in a simple and friendly tone for kids.

💻 Code Tips: In PHPDocs, define the intended audience and tone to guide LLM-generated documentation:

/**
 * Calculates the total energy output of a solar panel system.
 *
 * Intended Audience: Engineers and technical experts.
 * Tone: Formal and technical.
 *
 * @param float $panelArea The total area of solar panels in square meters.
 * @param float $efficiency The efficiency rate of the solar panels (0-1).
 * @param float $sunlightHours Daily sunlight hours.
 * 
 * @return float Total energy output in kilowatt-hours.
 */
function calculateSolarOutput(float $panelArea, float $efficiency, float $sunlightHours): float {
    return $panelArea * $efficiency * $sunlightHours;
}

🔍 9. Fine-Tuning and Domain Expertise

🧠 Theory: Fine-tuning allows LLMs to specialize in specific domains by further training them on domain-specific datasets. This enhances their ability to generate accurate, relevant, and nuanced outputs tailored to specialized tasks or fields.

⚙️ Technology:

  • Fine-tuning adjusts the weights of a pre-trained model by using a curated dataset that focuses on a specific domain.
  • This process requires labeled data and computational resources but significantly improves task performance in niche areas.

💡 Prompt Tips:

  • Use fine-tuning to simplify prompts for repeated tasks: Generate a legal brief summarizing the key points from this case.
  • Without fine-tuning, include detailed instructions and examples in your prompt: Write a summary of this legal case focusing on liability and negligence, using a formal tone.

💻 Code Tips: When fine-tuning is not an option, structure your PHPDocs to include domain-specific context for LLMs:

/**
 * Generates a compliance report for renewable energy projects.
 *
 * This function creates a detailed compliance report tailored for regulatory agencies. It checks for adherence to
 * energy efficiency standards and sustainability guidelines.
 *
 * @param array<string, mixed> $projectData Details of the renewable energy project.
 * @param string $region The region for which the compliance report is generated.
 * 
 * @return string The compliance report in a formatted string.
 */
function generateComplianceReport(array $projectData, string $region): string {
    // Example report generation logic.
    return sprintf(
        "Compliance Report for %s:\nProject: %s\nStatus: %s\n",
        $region,
        $projectData['name'] ?? 'Unnamed Project',
        $projectData['status'] ?? 'Pending Review'
    );
}

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