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building-with-llms

Architect prompting, agent orchestration, and evaluation frameworks for LLM applications.

Updated Jun 2, 2026
Search Tags:#workflow#rag#evaluation#agents#governance#prompts#llms
One-click install
npx skills add https://github.com/PSkinnerTech/lenny-skills --skill building-with-llms-pskinnertech
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: building-with-llms
Source: https://github.com/PSkinnerTech/lenny-skills/tree/main/skills/building-with-llms
Command: npx skills add https://github.com/PSkinnerTech/lenny-skills --skill building-with-llms-pskinnertech

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Building AI-powered applications using LLMs can be complex, error-prone, and time-consuming, requiring orchestration of prompts, models, and data sources.

Core Features & Use Cases

  • Architect prompting strategies, agent orchestration, and evaluation frameworks for AI product workflows.
  • Provide repeatable patterns for RAG, multi-agent coordination, and model governance across product teams.
  • Use Case: A product team builds an autonomous assistant with retrieval-augmented generation and self-improving prompts.

Quick Start

Set up an end-to-end AI assistant that uses a chat interface with retrieval and evaluation checks.

Frequently Asked Questions about building-with-llms

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an AI application with LLMs that handles prompting, agents, and evaluation?▼

You can build an AI application with LLMs by applying repeatable patterns for prompting strategies, agent orchestration, and structured evaluation workflows, streamlining the creation of chatbots and retrieval-augmented generation pipelines from prototyping to deployment.

What is the best way to architect multi-agent coordination and RAG pipelines for a product team?▼

Architecting multi-agent coordination and RAG pipelines involves using repeatable patterns for retrieval-augmented generation and model governance, allowing product, engineering, and design teams to build autonomous assistants with self-improving prompts.

Can I use this approach to set up an end-to-end AI assistant with retrieval and evaluation checks?▼

Yes, you can set up an end-to-end AI assistant using a chat interface integrated with retrieval-augmented generation and structured evaluation checks to ensure robust performance across prototyping and deployment scenarios.

Does this workflow support model governance and evaluation frameworks across engineering teams?▼

This workflow supports model governance by providing repeatable patterns and structured evaluation frameworks, ensuring product, engineering, and design teams can consistently orchestrate prompts, models, and data sources without errors.

When do I need structured evaluation workflows for LLM applications?▼

You need structured evaluation workflows for LLM applications when building autonomous assistants or complex retrieval-augmented generation pipelines, ensuring prompting strategies and multi-agent coordination perform reliably during deployment.

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