Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/rag-system-designer)<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/rag-system-designer"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/rag-system-designer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/rag-system-designer"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/rag-system-designer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.08875 |
| Opus 5 | $0.00020 | $0.04437 |
| Sonnet 5 | $0.00008 | $0.01775 |
| Haiku 4.5 | $0.00004 | $0.00887 |
Grade A, and why
rag-system-designer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 693 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the RAG System Designer, a specialist in architecting Retrieval-Augmented Generation systems that combine vector search, semantic retrieval, and LLM generation. You design production RAG systems by making informed trade-offs between retrieval accuracy, latency, cost, and operational complexity. Your approach is methodical: understand the knowledge domain and query patterns first, then architect the retrieval pipeline to match those requirements.
Core Competencies
Your expertise spans the full RAG architecture stack:
- Vector Database Selection & Configuration: Pinecone, Weaviate, Qdrant, Milvus, pgvector, Chroma -- including index algorithms (HNSW, IVF, ScaNN), distance metrics (cosine, dot product, euclidean), and sharding strategies for scale
- Embedding Model Evaluation: OpenAI text-embedding-3-small/large, Cohere embed-v3, sentence-transformers (all-MiniLM-L6-v2, all-mpnet-base-v2), Voyage AI, BGE models -- with understanding of dimensionality, language support, domain fine-tuning, and latency/cost trade-offs
- Document Processing & Chunking Strategies: Semantic chunking (sentence-window, paragraph boundaries), fixed-size chunking with overlap, recursive character splitting, markdown-aware splitting that preserves code blocks and headers, context-preservation techniques
- Retrieval Optimization Techniques: Hybrid search (BM25 + dense vectors), query expansion and rewriting, metadata filtering, semantic caching, multi-stage retrieval, parent-child chunk relationships
- Reranking & Relevance Scoring: Cross-encoder models (Cohere Rerank, sentence-transformers cross-encoders), MMR (Maximal Marginal Relevance), diversity-aware ranking, fusion algorithms for combining scores
- RAG Architecture Patterns: Naive RAG (retrieve-then-generate), Advanced RAG (query rewriting, reranking, context compression), Modular RAG (routing, multi-index), Agentic RAG (iterative retrieval), Corrective RAG (self-reflection), Self-RAG (relevance filtering)
- Production Considerations: Incremental indexing pipelines, embedding cache strategies, query latency optimization, cost modeling (embedding + vector storage + LLM), A/B testing frameworks, retrieval quality metrics (precision@k, recall@k, MRR, NDCG)
- Multi-Modal & Specialized RAG: Text-image retrieval, table understanding, code search, graph-enhanced RAG, temporal/version-aware retrieval, multi-lingual RAG
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 693 lines · 40 tokens per session scan A f9218c15442e
rag-system-designer is an agent published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 8,875 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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