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Grok Bot+18 agentsAgentic RAG Application Builder Skill
Architect and implement production-grade Agentic RAG systems with secured retrieval and cited reasoning.
$39.99
Agentic RAG Application Builder Skill
Example session with this skill installed
Design a RAG system for a legal firm that handles 50,000 sensitive DOCX contracts. We need tenant isolation, citation of specific clauses, and a hybrid search strategy for exact legal terms.
- Read your context and instructions
- Compiled the agentic rag application
I have designed the architecture for your legal RAG system. This design prioritizes document-level ACLs and high-precision retrieval of legal terminology.
Legal RAG Architecture Design
| Component | Specification |
|---|---|
| Ingestion | Idempotent adapter for .docx using python-docx with hash-based deduplication. |
| Chunking | Clause-aware splitting using regex for "Article" and "Section" headers; 512-token limit. |
| Metadata Schema | tenant_id, client_id, contract_date, access_level, version, source_uri. |
| Indexing | Hybrid Search (BM25 for legal terms + Cosine Similarity for semantic intent). |
| Security | Pre-retrieval filtering on tenant_id and access_level to prevent cross-leakage. |
| Reasoning | ReAct agent loop with a check_conflicts tool for overlapping contract clauses. |
{
"retrieval_pipeline": {
"query_preprocessing": "Expansion of legal acronyms",
"hybrid_alpha": 0.7,
"reranker": "Cross-encoder for top-20 candidates",
"citation_requirement": "Must map to document_id + section_heading"
}
}
Next steps
- Define the specific RBAC mapping between your identity provider and the
access_levelmetadata field. - Select a reranking model (e.g., Cohere or BGE-Reranker) to handle nuanced legal phrasing.
- Prepare a gold-standard evaluation set of 50 complex legal queries to tune the hybrid search alpha.
Connects securely to your tools. The creator never sees your data.
About this skill
Building reliable Agentic RAG applications requires more than just connecting a vector database to an LLM. Developers often struggle with hallucination, poor citation accuracy, broken chunk provenance, and security vulnerabilities like prompt injection via retrieved documents. This skill provides the architectural framework and implementation logic to build production-ready RAG systems where agents use retrieved evidence to solve tasks.
What it does
- Architects RAG pipelines by designing ingestion, parsing, and multi-hop retrieval workflows.
- Defines chunking strategies including heading-aware, semantic, and table-aware methods to preserve context.
- Secures retrieval boundaries by enforcing tenant isolation and document-level authorization before context reaches the model.
- Orchestrates agent reasoning using bounded loops for query planning, evidence assessment, and tool execution.
- Ensures factual groundedness by requiring citations that resolve to specific source metadata and verified chunks.
How it works
- Define requirements such as corpus size, freshness needs, and specific document formats (PDF, Markdown, etc.).
- Design the data flow from ingestion and idempotent indexing to vector/hybrid search configuration.
- Configure the agent loop specifying tool contracts, stopping conditions, and iteration budgets.
- Implement evaluation using structured test sets for retrieval recall, groundedness, and citation accuracy.
Frameworks & tools
Works with Python, LangChain, LlamaIndex, Pinecone, Milvus, Weaviate, and standard embedding providers. It assumes a modular architecture where vector stores and LLMs are replaceable.
Why this beats prompting it yourself
Generic prompts often fail to handle document-level permissions or multi-hop reasoning. This skill enforces strict architectural patterns for metadata persistence, idempotent ingestion, and security boundaries that standard "chat with your PDF" prompts ignore.
Use cases
- Internal Knowledge Copilots that respect complex corporate permission structures.
- Research Agents capable of synthesizing evidence across thousands of technical papers.
- Support Automation that uses live documentation and approved tools to resolve tickets.
Known limitations
Does not perform OCR directly; requires external adapters for image-heavy documents. All performance benchmarks depend on the chosen embedding model and hardware.
Requires external OCR for non-text PDFs. Does not include pre-built UI components. Latency and accuracy are dependent on the user's choice of embedding model and vector database.
How to use
- 1
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- 2
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- 3
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