arela/RESEARCH/prompts/meta-rag-research-prompt.md

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# Deep Research Request: Meta-RAG Implementation for Arela's Context Router

## Context

Arela is building an AI Technical Co-Founder tool with a tri-memory system:
1. **Vector Memory** (RAG) - Semantic search via embeddings
2. **Graph Memory** - Structural dependencies (imports, functions, calls)
3. **Governance Memory** - Audit log of decisions and changes

**Current limitation:** Simple retrieval - query → search embeddings → return top K chunks

**Goal:** Implement Meta-RAG to intelligently route queries to the right memory system and verify retrieval quality.

## Our Specific Use Case

**Scale:**
- Codebases: 50-20,000 files
- Languages: 15+ (TypeScript, Python, Go, Rust, etc.)
- Memory types: 3 distinct systems (vector, graph, audit)
- Query types: Factual, conceptual, comparative, procedural, temporal

**Requirements:**
- Fast (<500ms for query classification + routing)
- Accurate (>85% correct strategy selection)
- Self-correcting (iterative refinement if first retrieval fails)
- Cost-effective (minimize LLM calls)
- Local-first (works with Ollama, not just cloud APIs)

**Constraints:**
- Must work with existing JSON-based RAG index (46MB file)
- Must integrate with SQLite graph DB
- Must be language-agnostic (no AST parsing)
- Must run on developer laptops (no heavy compute)

## Research Questions

### 1. Meta-RAG Fundamentals
- What is the academic definition of Meta-RAG?
- How does it differ from traditional RAG, Agentic RAG, and Self-RAG?
- What are the key components (query understanding, strategy selection, quality verification)?
- What are the proven architectures (LlamaIndex, LangChain, custom)?

### 2. Query Classification
- How to classify queries into types (factual, conceptual, comparative, procedural, temporal)?
- Can this be done without an LLM (rule-based, small model, embeddings)?
- What are the accuracy benchmarks for query classification?
- How to handle ambiguous queries that span multiple types?

### 3. Strategy Selection & Routing
- How to map query types to retrieval strategies?
- When to use dense retrieval (embeddings) vs sparse retrieval (BM25/keyword)?
- When to use graph traversal vs vector search?
- How to combine multiple retrieval methods (fusion strategies)?
- What are the performance trade-offs (latency, accuracy, cost)?

### 4. Quality Verification
- How to verify if retrieved context is relevant to the query?
- What metrics to use (relevance score, completeness, diversity)?
- Can verification be done without an LLM (cheaper, faster)?
- How to detect hallucination risk from poor retrieval?

### 5. Iterative Refinement
- When to trigger a second retrieval pass?
- How to reformulate queries for better results?
- What are the stopping criteria (max iterations, quality threshold)?
- How to avoid infinite loops or excessive compute?

### 6. Multi-Memory Integration
- How to route queries across 3 different memory systems (vector, graph, audit)?
- How to fuse results from heterogeneous sources (embeddings + SQL + logs)?
- What are the best practices for hybrid retrieval?
- How to handle conflicts between memory systems?

### 7. Performance & Scalability
- What are the latency benchmarks for Meta-RAG systems?
- How to optimize for developer laptops (not cloud GPUs)?
- What are the memory requirements (RAM, disk)?
- How to handle large codebases (20k+ files)?

### 8. Implementation Approaches
- **LlamaIndex:** Does it support Meta-RAG? How mature is it?
- **LangChain:** What's their approach to query routing and verification?
- **Custom:** Should we build from scratch? What are the trade-offs?
- **Hybrid:** Can we use existing libraries for parts and custom for others?

### 9. Local Model Support
- Can Meta-RAG work with Ollama (local LLMs)?
- What are the accuracy trade-offs vs cloud models (GPT-4, Claude)?
- Can query classification use a small local model (Llama 3.2 1B)?
- How to minimize LLM calls (cost + latency)?

### 10. Edge Cases & Failure Modes
- What happens when all retrieval strategies fail?
- How to handle queries that need information not in the codebase?
- How to deal with outdated or stale context?
- What are the common failure patterns and mitigations?

## What We Need

### Academic Foundation
- 3-5 key papers on Meta-RAG, Self-RAG, or Agentic RAG
- Benchmarks comparing approaches (accuracy, latency, cost)
- Proven architectures and design patterns

### Practical Implementation
- Code examples or open-source implementations
- Step-by-step implementation guide for our use case
- Performance optimization techniques
- Testing and evaluation strategies

### Comparative Analysis
- Meta-RAG vs traditional RAG (when is it worth the complexity?)
- LlamaIndex vs LangChain vs custom (pros/cons for Arela)
- Local models vs cloud APIs (accuracy/cost/latency trade-offs)

### Integration Strategy
- How to integrate with existing JSON RAG index
- How to integrate with SQLite graph DB
- How to add without breaking current functionality
- Migration path from current system to Meta-RAG

## Success Criteria

A successful Meta-RAG implementation for Arela should:

1. **Improve answer quality by 30%+** (measured by relevance score)
2. **Reduce hallucinations by 50%+** (measured by verification failures)
3. **Add <200ms latency** (query classification + routing overhead)
4. **Work with local models** (Ollama, no cloud dependency)
5. **Handle 95%+ of query types** (factual, conceptual, comparative, etc.)
6. **Self-correct 80%+ of bad retrievals** (iterative refinement)
7. **Cost <$0.01 per query** (minimize LLM calls)

## Specific Questions for Validation

1. **Is Meta-RAG production-ready or still research?**
   - Are there companies using it at scale?
   - What are the known limitations?

2. **Can it work with our JSON-based RAG index?**
   - Or do we need to migrate to a proper vector DB first?
   - What's the minimum viable Meta-RAG architecture?

3. **Should we build or buy?**
   - Use LlamaIndex/LangChain or custom implementation?
   - What are the trade-offs (flexibility, maintenance, features)?

4. **What's the ROI?**
   - How much better will answers be?
   - Is the complexity worth it for v4.2.0?
   - Or should we wait for v5.0.0?

5. **How to test and evaluate?**
   - What metrics to track?
   - How to benchmark against current system?
   - What's a good test dataset?

## Expected Output

Please provide:

1. **Executive Summary** (1 page)
   - What is Meta-RAG and why it matters
   - Is it right for Arela's use case?
   - Recommended approach (build/buy/wait)

2. **Technical Deep Dive** (5-10 pages)
   - Architecture diagrams
   - Component breakdown (query classifier, router, verifier, refiner)
   - Integration with tri-memory system
   - Performance analysis

3. **Implementation Plan** (2-3 pages)
   - Phase 1: Query classification
   - Phase 2: Strategy router
   - Phase 3: Quality verification
   - Phase 4: Iterative refinement
   - Timeline and effort estimates

4. **Comparative Analysis** (2-3 pages)
   - LlamaIndex vs LangChain vs custom
   - Local models vs cloud APIs
   - Benchmarks and trade-offs

5. **Code Examples** (if available)
   - Query classifier implementation
   - Strategy router implementation
   - Quality verifier implementation
   - Integration examples

6. **Risk Assessment** (1 page)
   - What could go wrong?
   - Mitigation strategies
   - Fallback plans

7. **References**
   - Academic papers
   - Open-source implementations
   - Blog posts and case studies
   - Benchmarks and evaluations

## Context from Previous Research

We've already validated:
- **VSA + API-Contract-First architecture** (Research Paper 1)
- **Infomap for slice detection** (CASCADE-003 research)
- **3-layer architecture** (programmatic → small model → big model)
- **Token efficiency strategies** (IDs, hierarchical context, delta updates)

Meta-RAG fits into the **Layer 1: Small Local Model (Interpreter/Router)** from our 3-layer architecture. It's the intelligence that decides:
- Which memory system to query
- How to combine results
- Whether to refine the search

## Timeline

**Urgency:** Medium-High
- Not blocking v4.0.1 (slice extraction)
- Desired for v4.2.0 (intelligence layer)
- Research should complete in 1-2 days

## Audience

- **Primary:** Arela development team (technical implementation)
- **Secondary:** Potential users/investors (product differentiation)

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**Please research this comprehensively. This is a potential 10x improvement to Arela's context understanding and a major competitive differentiator vs Cursor/Copilot/Windsurf.**

**Focus on practical implementation over pure theory. We want to ship this, not just understand it.**