Dynamic Prompt Engineering - joehubert/ai-agent-design-patterns GitHub Wiki

Classification

Intent

To automatically reformulate or enhance prompts before sending them to underlying language models, optimizing for both quality and efficiency based on the request type, context, and requirements.

Also Known As

Adaptive Prompting, Context-Aware Prompt Optimization, Smart Prompting

Motivation

Language models are highly sensitive to how prompts are phrased and structured. A well-crafted prompt can dramatically improve response quality, while a poorly formulated one may lead to suboptimal outputs despite using the same underlying model. However, manually crafting optimal prompts for every request type is impractical, especially in production systems handling diverse queries.

Traditional static prompting approaches use fixed templates regardless of the request's nature, leading to:

Excessive token usage when simple requests receive unnecessarily complex prompts
Inadequate instructions for complex tasks requiring specialized guidance
Missed opportunities to incorporate relevant context or personalization
Inability to adapt to evolving user needs or emerging edge cases

Dynamic Prompt Engineering addresses these limitations by treating prompt construction itself as a programmatic process that can adapt based on request analysis, historical performance, and system state.

Applicability

When to use this pattern:

In systems handling diverse query types with varying complexity levels
When optimizing for both cost efficiency and response quality
For applications where user context significantly impacts optimal response formulation
When prompt templates need to evolve based on performance data
In multi-tenant systems where different users or use cases require specialized prompting strategies
When integrating with different underlying models that respond optimally to different prompt styles
For systems that need to balance token usage against output quality

Structure

To do...

Components

Request Analyzer: Examines incoming requests to determine their type, complexity, and special requirements, extracting key features that influence prompt selection.
Prompt Template Repository: Stores various prompt templates optimized for different request types and contexts, potentially organized hierarchically.
Context Manager: Maintains and retrieves relevant context from current and past interactions that should be incorporated into the prompt.
Rule Engine: Contains logic for selecting and applying appropriate templates and modifications based on the request analysis.
Token Optimizer: Estimates token usage and balances verbosity against cost considerations, potentially compressing or expanding prompts as needed.
Template Processor: Combines selected templates with contextual variables, performing substitutions and transformations.
Performance Monitor: Tracks the effectiveness of different prompt strategies to inform future prompt selection and refinement.

Interactions

The components work together in the following sequence:

The Request Analyzer examines the incoming user query to classify it and extract key features.
The Rule Engine selects appropriate templates and transformation rules based on the analysis.
The Context Manager retrieves relevant contextual information to be incorporated.
The Template Processor combines templates with context variables and applies transformations.
The Token Optimizer evaluates the resulting prompt for efficiency and may compress it if needed.
The final optimized prompt is sent to the language model.
The Performance Monitor records the prompt-response pair and effectiveness metrics.

Consequences

Benefits

Improved response quality by tailoring prompts to specific request types
Reduced token usage and costs by avoiding unnecessarily verbose prompts for simple queries
Adaptability to different user needs and contexts without manual intervention
Continuous optimization as the system learns which prompt strategies work best
Consistency in handling similar request types across the application
Scalability to handle new request types by adding templates rather than modifying core logic

Limitations

Added complexity in system design and maintenance
Potential overhead from the prompt analysis and construction process
Debugging challenges when responses are unexpected, as the actual prompt sent may differ from what developers expect
Need for monitoring to ensure dynamic modifications don't introduce unintended effects

Performance Implications

Small additional latency from the prompt processing step
Potential significant reduction in token usage for common request types
May require additional computational resources for analyzing requests and optimizing prompts
Overall system throughput can improve as more efficient prompts reduce model processing time

Implementation

Guidelines for implementing the pattern:

Start with request classification: Develop a robust taxonomy of request types your system handles.
Create specialized templates: Design base templates optimized for each major request category.
Implement gradual refinement: Begin with simple rule-based selection, then add more sophisticated transformations.
Build a feedback loop: Track which prompt strategies perform best for different request types.
Consider hierarchical templates: Use a system of base templates with specialized modifiers.
Implement safeguards: Ensure dynamic modifications don't remove critical instructions or constraints.
Balance optimization efforts: Focus most on high-frequency request types for efficiency gains.
Test systematically: Compare dynamic prompts against static baselines to verify improvements.

Common pitfalls to avoid:

Over-engineering prompts for simple requests
Neglecting to preserve critical instruction elements during optimization
Adding too many contextual elements that distract from the core task
Failing to account for model-specific prompt preferences

Code Examples

To do...

Variations

Pre-processor Pipeline

A variation where the request flows through a series of specialized processors, each adding specific elements to the prompt based on detection of particular features or requirements.

Template Selection with Fallbacks

Instead of modifying prompts, this variation focuses on selecting from a library of pre-optimized complete prompts with a fallback mechanism when none perfectly match.

A/B Testing Framework

This variation systematically tests different prompt formulations for similar requests to identify optimal strategies through empirical evidence.

Hybrid Human-AI Prompting

Combines automated prompt engineering with human review for high-stakes or novel request types, gradually shifting toward automation as patterns emerge.

Real-World Examples

Customer Support Systems: Dynamic prompting that incorporates customer history, product specifics, and detected sentiment to frame responses appropriately.
Content Creation Platforms: Systems that adjust instruction detail based on content type, adding specialized guidelines for technical articles versus creative writing.
Educational Applications: Learning platforms that progressively simplify prompts as students demonstrate mastery, providing more scaffolding for struggling learners.
Enterprise Search Applications: Query reformulation systems that enhance basic keyword searches with structured prompts incorporating department context and access permissions.

Related Patterns

Complexity-Based Routing: Often used alongside Dynamic Prompt Engineering to direct requests to appropriate models after prompt optimization.
Semantic Caching: Complements this pattern by storing optimized prompts and responses for similar future queries.
Reflection: Can be incorporated as a component of dynamic prompting, adding self-evaluation instructions for complex tasks.
Fallback Chains: Provides alternative paths when dynamically constructed prompts fail to produce satisfactory results.
Router: May precede Dynamic Prompt Engineering to determine which prompt strategy to apply based on request classification.