DSP Pipeline Architecture: Examples and Use Cases¶
Document Type: Knowledge Base - Pipeline Architecture Examples
Created: 2025-10-20
Last Updated: 2025-10-20
Confidence Level: High
Source: Synthesized from DSP research papers and framework analysis
Purpose: Provide concrete examples and use cases for DSP pipeline architecture implementation
Overview¶
This document provides detailed examples and real-world use cases of DSP pipeline architecture, demonstrating how the three fundamental operations (Demonstrate-Search-Predict) compose into sophisticated multi-stage reasoning systems.
Pipeline Architecture Fundamentals¶
Core Concept: Compositional Programming¶
DSP's pipeline architecture is built on systematic problem decomposition - breaking down complex knowledge-intensive tasks into smaller, manageable transformations that Language Models (LM) and Retrieval Models (RM) can handle reliably.
Key Principle: Natural language texts are passed between LM and RM in multiple, intricate steps, not just a single retrieve-then-read operation.
Architectural Philosophy¶
- Explicit Information Flow: Developer defines when and how components interact
- Natural Language Interface: Text-based communication between all components
- Grounded Processing: Every prediction explicitly based on retrieved evidence
- Pipeline Awareness: Demonstrations and predictions understand the full pipeline context
Concrete Pipeline Examples¶
Example 1: Multi-Hop Question Answering Pipeline¶
Task: Answer questions requiring information synthesis from multiple sources.
Question: "What year did the director of Inception win his first Oscar?"
Pipeline Architecture¶
Stage 1: SEARCH (Initial Information Gathering)
├─ Input Query: "Inception director"
├─ Retrieval Action: Search knowledge base for movie information
└─ Retrieved Context: "Christopher Nolan directed Inception (2010)..."
Stage 2: PREDICT (Intermediate Reasoning)
├─ Input: Original question + Retrieved context from Stage 1
├─ Task: Extract entity and formulate new search query
├─ Reasoning: Identify that Christopher Nolan is the director
└─ Output: New search query: "Christopher Nolan first Oscar win year"
Stage 3: SEARCH (Follow-up Information Gathering)
├─ Input Query: Generated query from Stage 2
├─ Retrieval Action: Search for specific person's achievements
└─ Retrieved Context: "Christopher Nolan won his first Academy Award in 2024 for Oppenheimer..."
Stage 4: PREDICT (Final Synthesis)
├─ Input: Original question + All retrieved contexts
├─ Task: Synthesize comprehensive, grounded answer
├─ Reasoning: Combine information from both retrieval steps
└─ Output: "2024 (Christopher Nolan, director of Inception, won his first Oscar for Oppenheimer)"
Key Innovation Points¶
- Multi-Stage Retrieval: Two separate search operations, each targeted at different information needs
- Intermediate Prediction: LM used to decompose complex question into manageable sub-questions
- Grounded Synthesis: Final answer explicitly references all retrieved evidence
- Natural Flow: Each stage builds on previous results through natural language
Performance Context¶
Multi-hop reasoning tasks showed 8-39% improvements over standard retrieve-then-read pipelines, demonstrating the value of sophisticated composition.
Example 2: Open-Domain Question Answering¶
Task: Answer general knowledge questions with retrieval augmentation.
Question: "What is the capital of the country where Mount Fuji is located?"
Pipeline Architecture¶
Stage 1: DEMONSTRATE
├─ Purpose: Load pipeline-aware examples
├─ Examples Include:
│ ├─ How to use retrieved context effectively
│ ├─ How to identify relevant information in passages
│ ├─ How to formulate grounded answers with evidence
│ └─ Patterns for geographic/factual queries
└─ Guidance: Shows the LM how to process this type of question
Stage 2: SEARCH
├─ Input Query: "Mount Fuji location capital"
├─ Retrieval Action: Search for geographic information
└─ Retrieved Context: [
│ "Mount Fuji is located on Honshu island in Japan...",
│ "Tokyo is the capital city of Japan...",
│ "Mount Fuji is Japan's tallest mountain..."
│ ]
Stage 3: PREDICT
├─ Input: Question + Demonstrations + Retrieved passages
├─ Process: Ground answer in retrieved evidence
├─ Reasoning Steps:
│ 1. Identify country from context (Japan)
│ 2. Locate capital information (Tokyo)
│ 3. Synthesize coherent answer with evidence
└─ Output: "Tokyo (Mount Fuji is located in Japan, and Tokyo is Japan's capital)"
Key Innovation Points¶
- Pipeline-Aware Demonstrations: Examples show how to use the entire pipeline, not just end-to-end examples
- Evidence Grounding: Answer explicitly references retrieved passages
- Clear Reasoning Path: Each step is transparent and verifiable
- Single-Pass Efficiency: For simpler questions, single retrieval may suffice
Performance Context¶
Open-domain QA demonstrated 37-120% relative gains over vanilla GPT-3.5, showing dramatic improvement through retrieval augmentation.
Example 3: Conversational Question Answering¶
Task: Answer questions in dialogue context where information needs evolve across turns.
Pipeline Architecture¶
Turn 1:
User: "Tell me about quantum computing."
Pipeline Execution:
├─ DEMONSTRATE: Load conversational QA examples
├─ SEARCH: Query "quantum computing basics overview"
├─ Retrieved: Comprehensive passages about quantum computing fundamentals
├─ PREDICT: Generate informative response grounded in retrieved context
└─ Output: [Detailed explanation with evidence]
Turn 2:
User: "Who invented it?"
Pipeline Execution:
├─ Context: Previous turn + conversation history
├─ PREDICT: Resolve reference ("it" = quantum computing from Turn 1)
├─ SEARCH: Query "quantum computing inventor history" + conversation context
├─ Retrieved: Information about early quantum computing pioneers
├─ PREDICT: Answer grounded in conversation history + new retrieval
└─ Output: "Multiple physicists contributed to quantum computing's development, including Richard Feynman (1981) and David Deutsch (1985)..."
Turn 3:
User: "What were their other contributions?"
Pipeline Execution:
├─ Context: Full conversation history
├─ PREDICT: Extract entities from conversation (Feynman and Deutsch)
├─ SEARCH: Targeted queries for each person's contributions
├─ Retrieved: Comprehensive information about both physicists' work
├─ PREDICT: Synthesize comprehensive answer using all conversation context
└─ Output: [Detailed response about both physicists' broader contributions]
Key Innovation Points¶
- Context Accumulation: Pipeline maintains and uses conversation history
- Reference Resolution: LM used to resolve pronouns and references before search
- Dynamic Retrieval: Search queries adapt based on conversation state
- Multi-Turn Coherence: Each response builds on previous context
Performance Context¶
Conversational QA achieved 80-290% relative gains vs. contemporary self-ask pipelines, showing exceptional performance in dialogue scenarios.
Real-World Use Cases¶
Use Case 1: Research Assistant System¶
Scenario: Academic research paper analysis and synthesis
Business Requirements: - Summarize recent research in specific domains - Synthesize information from multiple papers - Provide citations and evidence trails - Handle complex, multi-aspect queries
Pipeline Architecture¶
User Query: "Summarize recent advances in transformer architectures"
Stage 1: DEMONSTRATE
├─ Load Examples:
│ ├─ Good research summaries with proper structure
│ ├─ Citation formats and source attribution
│ ├─ Synthesis patterns for academic content
│ └─ Multi-paper integration techniques
└─ Purpose: Guide LM in academic writing patterns
Stage 2: SEARCH (Initial Pass - Broad Discovery)
├─ Query: "transformer architecture advances 2023-2024"
├─ Retrieval Scope: Recent papers, articles, preprints
└─ Retrieved: [10-20 relevant research papers]
Stage 3: PREDICT (Analysis & Decomposition)
├─ Input: Retrieved papers + demonstrations
├─ Tasks:
│ ├─ Extract key research themes
│ ├─ Identify major innovations
│ ├─ Recognize research clusters
│ └─ Generate follow-up queries for deeper investigation
└─ Output: [List of themes: "Efficient attention mechanisms", "Long-context modeling", ...]
Stage 4: SEARCH (Deep Dive - Targeted Investigation)
├─ Execute Refined Queries:
│ ├─ "efficient attention mechanisms transformers 2024"
│ ├─ "long context transformer architectures"
│ └─ [Additional targeted queries for each theme]
└─ Retrieved: [Detailed technical papers for each theme]
Stage 5: PREDICT (Synthesis & Generation)
├─ Input: All retrieved information + demonstrations
├─ Tasks:
│ ├─ Combine information from all retrieval passes
│ ├─ Organize by themes and importance
│ ├─ Generate comprehensive summary
│ └─ Include proper citations for all claims
└─ Output: [Comprehensive, grounded research summary with citations]
Benefits¶
- Systematic Information Gathering: Multiple targeted retrieval passes
- Grounded in Research: Every claim backed by retrieved papers
- Multi-Step Reasoning: Complex synthesis through pipeline stages
- Evidence Trail: Clear provenance for all information
Expected Performance¶
Based on multi-hop reasoning benchmarks, expect 8-39% improvement over simple retrieve-then-read approaches for complex research queries.
Use Case 2: Customer Support Knowledge Base¶
Scenario: Complex technical support requiring multiple information sources
Business Requirements: - Diagnose problems from customer descriptions - Retrieve relevant troubleshooting procedures - Provide step-by-step guidance - Ground responses in official documentation
Pipeline Architecture¶
Customer Query: "My device won't connect to WiFi after the update"
Stage 1: SEARCH (Problem Identification)
├─ Query: "WiFi connectivity issues after update [device model]"
├─ Retrieval Sources:
│ ├─ Known issues database
│ ├─ Troubleshooting guides
│ └─ Update release notes
└─ Retrieved: [Relevant known issues and initial troubleshooting steps]
Stage 2: PREDICT (Issue Analysis)
├─ Input: Customer query + Retrieved information
├─ Tasks:
│ ├─ Analyze symptoms against known issues
│ ├─ Determine most likely causes
│ ├─ Prioritize troubleshooting approaches
│ └─ Generate targeted queries for solutions
└─ Output: [Identified causes: "Driver incompatibility", "Network settings reset", ...]
Stage 3: SEARCH (Targeted Solutions)
├─ Execute Queries:
│ ├─ "WiFi driver reinstall procedure [device model]"
│ ├─ "network settings restore [device model]"
│ └─ [Additional solution-specific queries]
└─ Retrieved: [Detailed resolution procedures from official docs]
Stage 4: PREDICT (Response Generation)
├─ Input: All context + Retrieved solutions
├─ Tasks:
│ ├─ Synthesize step-by-step troubleshooting guide
│ ├─ Order steps by likelihood of success
│ ├─ Include explanations for each step
│ ├─ Provide alternative solutions
│ └─ Ground all steps in official documentation
└─ Output: [Comprehensive, actionable support response]
Benefits¶
- 8-39% Improvement: Over simple retrieve-then-read in support scenarios
- Multi-Pass Retrieval: Comprehensive solution gathering
- Grounded Responses: All steps verified against official docs
- Diagnostic Reasoning: LM helps analyze and prioritize issues
Business Impact¶
- Reduced escalation rates
- Faster issue resolution
- Consistent, documented solutions
- Improved customer satisfaction
Use Case 3: Legal Document Analysis¶
Scenario: Multi-document legal research and precedent analysis
Business Requirements: - Analyze complex legal questions - Find relevant precedents across multiple cases - Synthesize holdings and principles - Maintain citation accuracy and authority
Pipeline Architecture¶
Legal Query: "What precedents exist for patent disputes in AI training data?"
Stage 1: DEMONSTRATE
├─ Load Examples:
│ ├─ Legal reasoning patterns
│ ├─ Citation formats (Bluebook, etc.)
│ ├─ Authority hierarchies
│ ├─ Multi-document synthesis approaches
│ └─ Precedent analysis techniques
└─ Purpose: Guide LM in legal analysis methodology
Stage 2: SEARCH (Broad Discovery)
├─ Query: "patent disputes AI training data case law"
├─ Retrieval Sources:
│ ├─ Case law databases
│ ├─ Legal journals
│ ├─ Court opinions
│ └─ Legal analysis articles
└─ Retrieved: [Relevant cases and legal discussions]
Stage 3: PREDICT (Key Concept Extraction)
├─ Input: Retrieved cases + legal demonstrations
├─ Tasks:
│ ├─ Identify key legal concepts
│ ├─ Extract relevant case names
│ ├─ Recognize applicable doctrines
│ └─ Generate targeted queries for each precedent
└─ Output: [List of cases: "Authors Guild v. Google", "Oracle v. Google", ...]
Stage 4: SEARCH (Precedent Deep Dive)
├─ Execute Case-Specific Queries:
│ ├─ "Authors Guild v Google full opinion fair use"
│ ├─ "Oracle v Google API copyright implications"
│ └─ [Additional queries for each identified case]
└─ Retrieved: [Full context, holdings, and implications for each case]
Stage 5: PREDICT (Legal Analysis)
├─ Input: All retrieved case law + demonstrations
├─ Tasks:
│ ├─ Synthesize holdings from all precedents
│ ├─ Analyze relationships between cases
│ ├─ Identify trends and evolving principles
│ ├─ Apply principles to query context (AI training data)
│ └─ Ground every claim in retrieved case law
└─ Output: [Comprehensive legal analysis with proper citations]
Benefits¶
- Systematic Precedent Discovery: Multi-stage retrieval finds all relevant cases
- Deep Analysis: Multiple retrieval passes enable comprehensive understanding
- Citation Accuracy: Grounding ensures proper legal citation
- Relationship Mapping: Shows how precedents relate to each other
Legal Practice Impact¶
- More thorough legal research
- Reduced research time
- Comprehensive precedent analysis
- Reliable citation trails
Pipeline Design Patterns¶
Pattern 1: Iterative Refinement¶
Structure:
Use When: - Information needs evolve based on partial results - Initial query too broad or ambiguous - Requires progressive deepening of understanding
Example Scenario: - Start with general search - Use results to refine understanding - Execute targeted follow-up searches - Iteratively build comprehensive answer
Performance Characteristic: Maximizes relevance through progressive refinement
Pattern 2: Parallel Investigation¶
Structure:
┌─ SEARCH (aspect A) ─┐
PREDICT (decompose) ├─ SEARCH (aspect B) ─┤ → PREDICT (combine)
└─ SEARCH (aspect C) ─┘
Use When: - Complex questions with multiple independent sub-questions - Different aspects require different retrieval strategies - Can parallelize retrieval for efficiency
Example Scenario: - Decompose: "Compare X, Y, and Z" - Search for X, Y, and Z in parallel - Combine results into comparative analysis
Performance Characteristic: Efficiency through parallelization
Pattern 3: Hierarchical Reasoning¶
Structure:
SEARCH (overview)
↓
PREDICT (identify subtopics)
↓
├─ SEARCH (subtopic 1)
├─ SEARCH (subtopic 2)
└─ SEARCH (subtopic 3)
↓
PREDICT (synthesize hierarchy)
Use When: - Need to build understanding from general to specific - Topic has natural hierarchical structure - Require both breadth and depth
Example Scenario: - Get overview of broad topic - Identify key subtopics - Deep dive into each subtopic - Synthesize hierarchical understanding
Performance Characteristic: Comprehensive coverage with organized structure
Pattern 4: Context Accumulation (Conversational)¶
Structure:
Turn 1: SEARCH → PREDICT → [context]
Turn 2: [context] + PREDICT (resolve) → SEARCH → PREDICT → [context]
Turn 3: [context] + PREDICT (resolve) → SEARCH → PREDICT → [context]
Use When: - Multi-turn conversations - References to previous context - Evolving information needs across turns
Example Scenario: - Initial question establishes context - Follow-up questions reference previous turns - Pipeline maintains and uses conversation history
Performance Characteristic: 80-290% gains in conversational settings
Key Architectural Principles¶
1. Pipeline-Aware Demonstrations¶
Traditional Approach: - Show examples of final answers only - LM learns simple input → output mapping - No guidance on intermediate steps
DSP Approach: - Show examples of each step in the pipeline - Demonstrate how to use retrieved context - Show intermediate reasoning steps - Guide the LM through the entire process
Example Demonstration Structure:
Demo 1: Search Query Formulation
Question: "Who wrote the book that inspired the movie Blade Runner?"
Step: Extract entity → "movie Blade Runner original book"
Demo 2: Context Usage
Retrieved: "Blade Runner (1982) was based on Philip K. Dick's novel Do Androids Dream of Electric Sheep?"
Step: Extract author from context → "Philip K. Dick"
Demo 3: Answer Synthesis
Question + Context → "Philip K. Dick (author of Do Androids Dream of Electric Sheep?, which inspired Blade Runner)"
Benefits: - LM understands how to perform each pipeline stage - More reliable intermediate transformations - Better context utilization - Reduced hallucination through grounding
2. Grounded Predictions¶
Principle: Every prediction is explicitly based on: - Retrieved passages (evidence) - Demonstrated patterns (guidance) - Previous pipeline steps (context)
Implementation Approach:
Prediction Input = {
original_query,
demonstrations: [pipeline_aware_examples],
retrieved_context: [passages_from_search],
pipeline_state: [previous_step_outputs]
}
Benefits: - More reliable outputs - Explainable predictions - Reduced hallucination - Evidence trail for verification
3. Explicit Information Flow¶
Developer Control Points: 1. When to search: Define search trigger conditions 2. What to search for: Specify query formulation strategy 3. How to process results: Define transformation logic 4. When to generate outputs: Specify prediction conditions 5. How to synthesize: Define combination strategies
Example Control Flow:
// Conceptual explicit flow definition
if needs_background_info(query) {
context = search(formulate_initial_query(query));
}
if needs_multi_hop(query, context) {
entities = predict_extract_entities(query, context);
additional_context = search(formulate_follow_up_query(entities));
context = merge(context, additional_context);
}
answer = predict_synthesize(query, context, demonstrations);
Benefits: - Full transparency - Predictable behavior - Easy debugging - Clear reasoning path
4. Natural Language Interfaces¶
Principle: All communication between components uses natural language text, not embeddings or structured data.
Interface Design:
Component Communication:
- LM → Text Query → RM
- RM → Text Passages → LM
- LM → Text Transformation → Next Stage
- All intermediates are human-readable text
Benefits: - Human-interpretable pipeline state - Flexible composition - Easy inspection and debugging - No format conversion overhead
Performance Characteristics¶
Documented Improvements¶
| Baseline System | DSP Improvement | Task Type | Strategic Insight |
|---|---|---|---|
| Vanilla GPT-3.5 | 37-120% | Open-domain QA | Value of retrieval augmentation |
| Retrieve-then-Read | 8-39% | Multi-hop reasoning | Sophisticated composition matters |
| Self-Ask Pipeline | 80-290% | Conversational QA | DSP paradigm superiority |
Performance Drivers¶
Why These Gains?
- Multi-Step Retrieval: Finds more relevant information through iterative refinement
- Pipeline-Aware Demonstrations: Guides effective context usage at each stage
- Grounded Predictions: Reduces hallucination by grounding in evidence
- Systematic Decomposition: Makes complex tasks manageable through breakdown
Performance Targets for AirsDSP¶
Based on original DSP benchmarks:
- Minimum Target: 8% improvement over retrieve-then-read baseline
- Typical Target: 20-40% improvement in multi-hop scenarios
- Stretch Target: Match or exceed 37-120% gains in open-domain tasks
AirsDSP Implementation Vision¶
Conceptual Rust API Design¶
// High-level pipeline composition
let pipeline = Pipeline::new()
.demonstrate(examples) // Load pipeline-aware demonstrations
.search(initial_query) // First retrieval pass
.predict(extract_entities) // Extract key information
.search(refined_query) // Follow-up targeted retrieval
.predict(synthesize_answer) // Final grounded prediction
.execute(question)?;
// Multi-hop reasoning example
let multi_hop = Pipeline::new()
.demonstrate(multi_hop_examples)
.search(Query::from_question(&question))
.predict(EntityExtraction::new())
.search(Query::from_entities)
.search(Query::from_entities) // Multiple parallel searches
.predict(Synthesis::with_grounding())
.execute(question)?;
// Conversational pipeline with context
let conversational = Pipeline::new()
.with_context(conversation_history)
.demonstrate(conversational_examples)
.predict(ReferenceResolution::new())
.search(Query::contextual)
.predict(GroundedResponse::with_history())
.update_context()
.execute(user_input)?;
Implementation Goals¶
- Explicit Control: Clear, understandable pipeline definition
- Type Safety: Compile-time guarantees for pipeline correctness
- Zero-Cost Abstractions: Efficient execution without runtime overhead
- Transparency: Full visibility into each stage
- Composability: Easy to build complex pipelines from simple operations
- Rust Idioms: Leverage Result types, traits, and ownership
Architectural Considerations¶
Type System Usage:
// Strong typing for pipeline stages
trait Demonstrate {
fn bootstrap(&self, examples: &[Example]) -> Result<Demonstrations>;
}
trait Search {
fn retrieve(&self, query: &Query) -> Result<Context>;
}
trait Predict {
fn generate(&self, input: &PredictInput) -> Result<Prediction>;
}
// Pipeline composition with type safety
struct Pipeline<S> {
state: S,
// State transitions enforced at compile time
}
Performance Optimization: - Zero-cost abstractions for composition - Efficient text processing - Concurrent execution where applicable - Memory-efficient context management
Comparison with Alternative Approaches¶
DSP vs. Simple Retrieve-Then-Read¶
| Aspect | Retrieve-Then-Read | DSP Pipeline |
|---|---|---|
| Retrieval Passes | Single pass | Multiple targeted passes |
| LM Usage | Final generation only | Intermediate + final steps |
| Demonstrations | End-to-end examples | Pipeline-aware examples |
| Information Flow | Linear | Multi-stage with feedback |
| Performance | Baseline | 8-39% improvement |
DSP vs. Vanilla Language Models¶
| Aspect | Vanilla LM | DSP Pipeline |
|---|---|---|
| External Knowledge | None | Retrieval-augmented |
| Grounding | No explicit grounding | Evidence-based predictions |
| Reasoning Steps | Opaque | Explicit pipeline stages |
| Performance | Baseline | 37-120% improvement |
DSP vs. Other Pipelines (e.g., Self-Ask)¶
| Aspect | Other Pipelines | DSP Pipeline |
|---|---|---|
| Composition | Fixed patterns | Flexible composition |
| Demonstrations | Limited or none | Pipeline-aware |
| Multi-hop | Basic support | Sophisticated multi-stage |
| Performance | Contemporary baseline | 80-290% improvement |
Implementation Priorities for AirsDSP¶
Phase 1: Core Operations¶
- Demonstrate Implementation
- Example loading and management
- Pipeline-aware demonstration structure
-
Context injection mechanisms
-
Search Implementation
- RM integration interface
- Query formulation support
-
Context retrieval and management
-
Predict Implementation
- LM integration interface
- Grounded prediction generation
- Context-aware processing
Phase 2: Pipeline Composition¶
- Composition API
- Pipeline builder pattern
- Stage composition primitives
-
Type-safe transitions
-
Execution Engine
- Stage orchestration
- Context threading
-
Error handling
-
State Management
- Pipeline state tracking
- Context accumulation
- Intermediate result storage
Phase 3: Advanced Patterns¶
- Design Pattern Support
- Iterative refinement helpers
- Parallel investigation support
- Hierarchical reasoning templates
-
Conversational context management
-
Performance Optimization
- Efficient text processing
- Concurrent execution support
- Memory optimization
-
Caching strategies
-
Developer Experience
- Clear error messages
- Pipeline visualization
- Debugging support
- Comprehensive documentation
Testing and Validation¶
Performance Benchmarks¶
Target Metrics: - Open-domain QA: Aim for 37%+ improvement over vanilla LM - Multi-hop reasoning: Target 8%+ improvement over retrieve-then-read - Conversational QA: Pursue 80%+ improvement over baselines
Benchmark Datasets: - Use standard NLP benchmark datasets - Create AirsDSP-specific test suites - Include multi-hop reasoning challenges
Validation Approach¶
- Correctness Testing
- Unit tests for each operation
- Integration tests for pipelines
-
End-to-end scenario validation
-
Performance Testing
- Benchmark against baselines
- Compare with original DSP results
-
Measure Rust-specific improvements
-
Developer Experience Testing
- API usability evaluation
- Documentation completeness
- Example pipeline clarity
References and Related Documentation¶
Primary Research Sources¶
- Khattab, O., et al. (2022). Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP. arXiv:2212.14024 [cs.CL].
Related Knowledge Base Documents¶
- DSP Framework Core:
dsp_framework_core.md - DSP Original Paper Detailed:
dsp_original_paper_detailed.md - DSP Paper Comprehensive Analysis:
dsp_paper_comprehensive_analysis.md - DSP/DSPy Evolution:
dsp_dspy_evolution.md - DSP/DSPy Comparative Evolution:
dsp_dspy_comparative_evolution.md
Implementation References¶
- AGENTS.md: Project-level guidance and standards
- Memory Bank: Sub-project context and decisions
- Workspace Standards: Code quality and enforcement guidelines
Key Takeaways¶
For Architecture Design¶
- ✅ Multi-stage pipelines dramatically outperform simple approaches
- ✅ Pipeline-aware demonstrations are critical for effectiveness
- ✅ Grounded predictions reduce hallucination and improve reliability
- ✅ Explicit control enables transparency and debuggability
- ✅ Natural language interfaces provide flexibility and composability
For Implementation¶
- ✅ Three operations are the foundation: Demonstrate, Search, Predict
- ✅ Composition patterns enable sophisticated reasoning flows
- ✅ Type safety can enforce correct pipeline construction
- ✅ Zero-cost abstractions maintain Rust performance advantages
- ✅ Clear APIs are essential for developer adoption
For Performance¶
- ✅ 37-120% gains over vanilla LMs validate retrieval augmentation
- ✅ 8-39% gains over retrieve-then-read justify sophisticated composition
- ✅ 80-290% gains in conversational settings show pattern strengths
- ✅ Multi-hop reasoning benefits most from pipeline sophistication
- ✅ Evidence grounding improves reliability and explainability
Document Status: Complete
Implementation Readiness: High - Ready for architecture design and implementation
Next Steps: Use these examples to guide AirsDSP pipeline API design and implementation priorities