A professional toolkit for optimizing end-to-end accuracy in Retrieval-Augmented Generation (RAG) systems across specialized domains.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install rag-accuracy-optimizer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install rag-accuracy-optimizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is RAG Accuracy Optimizer?
The RAG Accuracy Optimizer is a comprehensive Openclaw Skills module designed to refine every stage of the RAG lifecycle, from initial data design to production monitoring. It provides developers with the architectural blueprints and technical strategies needed to eliminate hallucinations and maximize retrieval precision. By addressing the critical bottleneck of 'context quality,' this skill ensures that AI agents have access to the most relevant, high-fidelity information.
This skill is particularly powerful for industries requiring high data integrity, such as finance, insurance, and healthcare. It bridges the gap between raw unstructured data and actionable AI insights by implementing advanced patterns like hybrid search (Vector + BM25), hierarchical chunking, and multi-query reformulation. Using these Openclaw Skills techniques, developers can significantly reduce latency and operational costs while maintaining a 90%+ answer accuracy rate.
RAG Accuracy Optimizer Use Cases
- Designing or improving a high-stakes RAG pipeline for insurance or legal documentation.
- Choosing and implementing optimal chunking strategies for long-form technical transcripts.
- Optimizing retrieval accuracy using hybrid search, reranking, and query expansion.
- Evaluating chunk quality and testing end-to-end system accuracy with ground-truth datasets.
- Implementing cost-optimized multi-model routing for large-scale AI orchestrators.
How RAG Accuracy Optimizer Works
- Structured Data Design: Defines the optimal balance between SQL and Vector databases based on the domain (e.g., insurance, finance).
- Strategic Chunking: Applies semantic or fixed-size chunking with 10-20% overlap to preserve context boundaries.
- Retrieval Pipeline: Executes a multi-stage process including query rewriting, metadata filtering, and hybrid search.
- Reranking & Compression: Re-sorts the top results using models like Cohere or Cross-encoders and compresses context to reduce noise.
- Generation & Safeguards: Generates answers using citation enforcement and hallucination prevention prompts.
- Evaluation: Measures performance using metrics like Precision@K, MRR, and NDCG through the integrated testing framework.
RAG Accuracy Optimizer Setup
To begin optimizing your RAG system with Openclaw Skills, install the required dependencies and run the evaluation scripts:
# Install core dependencies
pip install ragas langchain-openai datasets sentence-transformers numpy torch
# Run the RAGAS evaluation pipeline
python3 scripts/eval_ragas.py --test-file eval_dataset.json --metrics faithfulness,answer_relevancy
# Benchmark embedding models on your specific dataset
python3 scripts/embedding_benchmark.py --models bge-m3,multilingual-e5 --dataset custom_pairs.json
RAG Accuracy Optimizer Data Schema & Taxonomy
The skill organizes RAG data through a rigorous metadata taxonomy and domain-specific schemas:
| Metadata Key | Description | Purpose |
|---|---|---|
source |
Origin file name | Traceability and Citations |
entity_id |
Unique ID (e.g., Ticker, Policy #) | Pre-filtering search space |
chunk_index |
Position in document | Surrounding context retrieval |
keywords |
LLM-generated terms | Supports BM25 keyword search |
Schema Patterns:
- Finance: Links securities to reports and sections for granular financial analysis.
- Healthcare: Organizes drugs and guidelines by evidence level and interaction severity.
RAG Accuracy Optimizer Advanced Features
- Late Chunking: Embedding the entire document before pooling to retain multi-paragraph context.
- RAPTOR: Recursive abstractive processing to build multi-level summary trees for broad queries.
- GraphRAG: Knowledge graph construction for complex multi-hop reasoning synthesis.
- Multi-Model Orchestrator: 2-stage query classification to route tasks between cheap (Gemini Flash) and premium (Claude Sonnet) models.
- Vietnamese-Specific NLP: Dual indexing and word segmentation optimized for Vietnamese RAG systems.
SKILL.md
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