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Advanced Prompt Engineering#

The LIT Platform provides sophisticated tools for prompt engineering, allowing you to create dynamic, context-aware prompts that maximize the effectiveness of language models. Our unique approach combines static text with embedded Python code blocks that execute at runtime, enabling truly dynamic prompt generation.

Dynamic Prompt Templates#

Basic Structure#

A prompt template in the LIT Platform consists of:

  • Static text for unchanging instructions
  • Dynamic variables that change based on context
  • Python code blocks for complex logic
  • Conditional sections that adapt to inputs

Syntax#

This is static text that remains the same.

{state.user_name}, here is the data you requested:

{
# This is a Python code block
def format_data(data):
    result = []
    for item in data.items():
        result.append(f"- {item[0]}: {item[1]}")
    return "\n".join(result)

formatted_data = format_data(state.data)
}

{formatted_data}

Please let me know if you need anything else.

Python Code Islands#

The most powerful feature of LIT's prompt engineering system is the ability to embed Python code within prompts. These "code islands" execute when the prompt is generated, allowing for:

  • Complex data transformations
  • Conditional content generation
  • Real-time data integration
  • Mathematical calculations
  • Natural language processing

Available Context#

Code islands have access to:

  • state: The current workflow state
  • datetime, pandas, numpy: Common Python libraries
  • Custom helper functions defined in your project

Example: Data Analysis Prompt#

I want you to analyze the following financial data:

{
import pandas as pd
import numpy as np

# Process the financial data
df = pd.DataFrame(state.financial_data)
total = df['amount'].sum()
average = df['amount'].mean()
max_value = df['amount'].max()
min_value = df['amount'].min()
recent_trend = "upward" if df['amount'].iloc[-3:].mean() > df['amount'].iloc[-6:-3].mean() else "downward"

summary = f"""
Total: ${total:,.2f}
Average: ${average:,.2f}
Maximum: ${max_value:,.2f}
Minimum: ${min_value:,.2f}
Recent Trend: {recent_trend}
"""
}

{summary}

Based on this analysis, provide insights about the financial health of the company.

Testing and Iteration#

Prompt Testing Interface#

The LIT Platform includes a dedicated interface for testing and refining prompts:

  1. Navigate to LLM Studio > Prompt Lab
  2. Create or select a prompt template
  3. Provide sample input data
  4. Click "Generate" to see the resulting prompt
  5. Test the prompt with different models
  6. Compare results side-by-side

Versioning and History#

  • Each prompt version is saved automatically
  • Compare different versions to see what changed
  • Revert to previous versions if needed
  • Add notes to track your thought process

Prompt Library#

The LIT Platform includes a growing library of prompt templates for common use cases:

  • Summarization: Templates for different types of content summarization
  • Classification: Prompts for categorizing content
  • Extraction: Templates for extracting structured data from text
  • Creative: Prompts for creative writing and ideation
  • Analytical: Templates for data analysis and insights

Best Practices#

General Guidelines#

  • Be Specific: Clear instructions lead to better results
  • Use Examples: Include examples of desired outputs
  • Structure Matters: Organize prompts with clear sections
  • Test Edge Cases: Verify behavior with unusual inputs
  • Iterate: Continuously refine based on results

Code Island Tips#

  • Keep Code Simple: Focus on readability
  • Handle Errors: Add error handling for robustness
  • Minimize Dependencies: Use standard libraries when possible
  • Cache Results: Store complex calculations in variables
  • Add Comments: Document your code for future reference

Integration with Workflows#

Prompts can be used in various parts of the LLM Workflow Canvas:

  • As standalone LLM Prompt components
  • Within System Message components
  • As part of Few-Shot Example templates
  • In Response Formatter components

By combining the power of visual workflows with dynamic prompt engineering, the LIT Platform enables the creation of sophisticated LLM applications that can adapt to any use case.