llm-app-patterns
Architecture and integration sketches for LLM applications, with explicit retrieval, tool, privacy and verification boundaries.
Data & AnalyticsProduct ManagementAI & Automation
🤖 LLM Application Patterns
Architecture and integration sketches for LLM applications, with explicit retrieval, tool, privacy and verification boundaries.
Detailed Guide
Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
When to Use This Skill
Use this skill when:
- Designing LLM-powered applications
- Implementing RAG (Retrieval-Augmented Generation)
- Building AI agents with tools
- Setting up LLMOps monitoring
- Choosing between agent architectures
Inputs, worked example and verification
Record provider/SDK versions, authorized data and tools, request/response schemas, latency/cost budgets and the expected task outcome. All code above is an integration sketch: llm, database, parser and provider-response adapters are project-owned and must be implemented explicitly. Never execute model-provided Python, expressions or fuzzy tool names; dispatch only exact registered tools after schema and authorization checks. Enforce per-call deadlines as well as loop limits.
Example: a planner first queues steps A and B, then after A replaces the remaining work with C. The executor must run A then C, neve
Subscribers only
The full skill, its 2 bundled files and every download is included with every paid Complete AI plan.
Details
| Source | community |
|---|---|
| License | — |
| Risk label | critical ("critical" means the skill may run commands or touch files — read before use) |
| Files | SKILL.md, references/detailed-guide.md |
| Added | 2026-02-27 |
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