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building-with-llms

Guide product teams through LLM workflows, architecture, and evaluation.

1.2k|156|Updated Jan 29, 2026
Search Tags:#architecture#llm#rag#evaluation#agents#prompting
One-click install
npx skills add https://github.com/RefoundAI/lenny-skills --skill building-with-llms-refoundai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: building-with-llms
Source: https://github.com/RefoundAI/lenny-skills/tree/main/skills/building-with-llms
Command: npx skills add https://github.com/RefoundAI/lenny-skills --skill building-with-llms-refoundai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps product teams and AI builders create robust AI applications by applying practical LLM workflows, prompts, architecture patterns, and evaluation methods.

Core Features & Use Cases

  • Understands user goals and selects appropriate prompting and architectural approaches for AI features (chatbots, agents, RAG, eval pipelines).
  • Guides end-to-end product workflows from problem framing to evaluation, with best practices and common mistakes.
  • Provides actionable examples and a framework for iterative experimentation in AI-enabled products.

Quick Start

Tell me your AI product goal and I will outline a prompting strategy, architecture plan, and evaluation approach.

Frequently Asked Questions about building-with-llms

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

FAQPage Schema
How do I design the architecture for an LLM application with autonomous agents?▼

Designing LLM application architecture requires layering context, tools, and orchestration to support autonomous agents. Applying practical workflows guides teams from problem framing to evaluation, ensuring robust feature delivery.

What is the best way to build a RAG pipeline for an AI product?▼

Building a RAG pipeline effectively requires applying practical LLM workflows and architectural layering. Combining prompting discipline with structured evaluation methods ensures robust retrieval-augmented generation for AI products.

How do I create an evaluation approach for an LLM chatbot?▼

Creating an evaluation approach for an LLM chatbot involves setting up iterative experimentation frameworks. Applying evaluation principles and best practices helps teams measure and iterate on AI-driven tools effectively.

What prompting strategy should I use for my AI application?▼

Choosing a prompting strategy for an AI application relies on understanding user goals and applying prompting discipline. It outlines appropriate architectural approaches for chatbots, agents, and RAG pipelines.

Does this approach work for building AI features in enterprise environments?▼

Yes, this approach works for building AI features in enterprise environments. It applies cross-functional collaboration and architectural layering to satisfy requirements for prompting discipline and evaluation in startups or enterprises.

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