LLM-OPS — Setup Guide¶
Prerequisites¶
- Node.js 18+ (for npx)
- Any supported agent: Claude Code, Antigravity, Cursor, Gemini CLI, or Codex CLI
- Basic understanding of LLM concepts (RAG, embeddings, vector DBs)
Capabilities¶
| Capability | Description |
|---|---|
| RAG Implementation | Build retrieval-augmented generation pipelines |
| Embeddings | Create and manage embeddings with Pinecone, Chroma, pgvector |
| Vector Databases | Select and configure the right vector store for your use case |
| Fine-Tuning | Fine-tuning workflows for domain-specific models |
| Prompt Engineering | Advanced prompt design patterns |
| Cost Management | Track and reduce LLM API costs |
| Quality Evaluations | Eval frameworks for model output quality |
| Semantic Caching | Cache LLM responses to reduce latency and cost |
| Streaming | Real-time streaming response architectures |
| Agent Architectures | Production AI agent design patterns |
Installation¶
npx skills add sickn33/antigravity-awesome-skills/llm-ops
Quick Start¶
Trigger the skill when you need specialized LLM operations assistance:
- "Set up a RAG pipeline for our documentation"
- "Compare Pinecone vs Chroma for our use case"
- "Optimize our LLM costs — we're spending too much on API calls"
- "Design an eval framework for our agent responses"
- "Implement semantic caching for repeated queries"
Key Patterns¶
RAG Pipeline Architecture¶
Documents → Chunking → Embeddings → Vector Store → Retrieval → LLM → Response
Cost Optimization Strategies¶
| Strategy | Savings | Complexity |
|---|---|---|
| Semantic caching | 30-60% | Low |
| Prompt compression | 20-40% | Medium |
| Model routing (cheap model for easy queries) | 40-70% | Medium |
| Batch processing | 50% | Low (if latency-tolerant) |
| Fine-tuned small models | 60-80% | High |
Vector Database Selection¶
| Database | Best For | Limitations |
|---|---|---|
| Pinecone | Managed, zero-ops, fast | Cost at scale, vendor lock-in |
| Chroma | Local dev, open source | Not production-hardened |
| pgvector | Postgres-native, SQL queries | Slower than purpose-built |
| Weaviate | Hybrid search, GraphQL | Complex setup |
| Qdrant | Rust performance, filtering | Smaller ecosystem |
CorpusIQ Use Cases¶
-
Agent Knowledge Base: Build RAG pipelines over the CorpusIQ docs, Hermes operating protocols, and governance rules — agents query their own knowledge base instead of relying on training data.
-
Cost Tracking: Monitor and optimize LLM spend across all CorpusIQ agents (Sonnet, Opus, DeepSeek, Qwen). Route queries to the cheapest capable model.
-
Quality Evaluations: Implement eval suites for agent responses — measure accuracy, helpfulness, and safety before deploying new agent versions.
-
Semantic Caching: Cache frequent agent queries (e.g., "What's the preflight gate?") to reduce API costs by 30-60%.
Troubleshooting¶
| Symptom | Likely Cause | Fix |
|---|---|---|
| Embeddings dimension mismatch | Wrong model selected | Match embedding dimension to vector DB config |
| RAG returns irrelevant chunks | Chunk size too large/small | Tune chunk size (500-1000 tokens optimal) |
| Cost still high after caching | Cache miss rate too high | Widen cache key matching (semantic, not exact) |
| Fine-tuning job fails | Insufficient training data | Minimum 100 examples for LoRA fine-tuning |
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