Research

Foundational research and analysis that guides AirsDSP development.

Overview

This section contains comprehensive research on the Demonstrate-Search-Predict (DSP) framework, including analysis of the original paper, comparative studies, and architectural insights.

Research Categories

Core Framework Research

Understanding the foundational DSP framework and its principles.

• DSP Framework Core - Core concepts and principles of the DSP framework
• DSP Original Paper Analysis - Detailed analysis of the foundational research paper
• DSP Paper Comprehensive Analysis - In-depth examination of all aspects

Evolution & Comparison

How DSP evolved and how it compares to related approaches.

• DSP to DSPy Evolution - The evolution from DSP to DSPy
• DSP and DSPy Comparative Analysis - Detailed comparison between frameworks
• DSPy Framework Analysis - Deep dive into DSPy specifics

Architecture & Design

Architectural patterns and design strategies for DSP implementations.

• DSP Layered Architecture - Layered architecture design patterns
• Multi-Task System Architecture - System architecture for multiple tasks
• Pipeline Architecture Examples - Concrete pipeline architecture examples

Implementation Strategies

Practical strategies for implementing DSP components.

• Prompt Engineering Strategy - Effective prompt engineering approaches
• Reasoning Strategies - Implementation of reasoning patterns
• Retrieval Strategies - Retrieval integration strategies

Product Strategy

Strategic positioning and differentiation for AirsDSP.

• AirsDSP Product Differentiation - Strategic positioning vs DSPy
• Accuracy Through Architecture - How architecture drives accuracy

Research Purpose

This research serves to:

1. Foundation: Provide comprehensive understanding of DSP principles
2. Guidance: Inform implementation decisions with research-backed insights
3. Differentiation: Clarify AirsDSP's unique approach vs DSPy
4. Performance: Document expected performance characteristics
5. Architecture: Guide architectural and design decisions

Using This Research

For Contributors

• Read core framework research before implementation
• Reference architectural patterns during design
• Use comparative analysis to understand trade-offs
• Apply implementation strategies in code

For Users

• Understand the theoretical foundation
• Learn why AirsDSP makes certain design choices
• Compare with DSPy to choose the right tool
• See expected performance characteristics

Research Methodology

All research documents:

• ✅ Based on published papers and official sources
• ✅ Include citations and references
• ✅ Provide concrete examples where possible
• ✅ Document both strengths and limitations
• ✅ Connect theory to practical implementation

Key Insights

DSP Core Principles

1. Systematic Decomposition: Break complex tasks into manageable steps
2. Strategic Retrieval: Place retrieval where it provides maximum value
3. Demonstration Guidance: Use examples to constrain model behavior
4. Natural Language Interface: Components communicate through text
5. Frozen Models: No fine-tuning required

Performance Expectations

Based on original DSP research:

• 37-120% relative improvement over vanilla LMs
• 8-40% gains over simple retrieve-then-read
• Comparable accuracy to fine-tuned models without training

AirsDSP Differentiation

• Explicit Control: vs DSPy's automated optimization
• Architecture-Driven: Accuracy through design, not compilation
• Production Focus: Predictable behavior for reliability
• Rust-Native: Type safety and zero-cost abstractions

Contributing Research

Found relevant research or want to add analysis?

See Contributing Guide for details on how to contribute to the project.

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Updated: December 2025
Total Documents: 14 research papers
Coverage: Complete DSP framework foundation