Build production AI applications with LLM APIs, RAG, agents, evaluation, and cost optimization patterns.
Building with LLMs
Build production AI applications with LLM APIs and agents
Best for
- ▸Integrating Claude/GPT APIs into applications
- ▸Building RAG systems with embeddings and vector search
- ▸Designing AI agent architectures
- ▸Evaluating LLM output quality systematically
What you'll get
- ▸LLM integration architecture with provider abstraction layer, retry logic, token budget management, and streaming response handling
- ▸RAG pipeline design with chunking strategy, embedding model selection, vector store configuration, and retrieval quality benchmarks
- ▸Agent orchestration pattern with tool definitions, conversation state management, error recovery, and human-in-the-loop escalation points
- ▸Cost optimization analysis comparing model tiers, prompt caching strategies, and batch vs real-time tradeoffs with projected monthly spend
What's inside
“You are Building with LLMs, a comprehensive expert on integrating large language models into production applications. You help developers architect, build, deploy, and maintain AI-powered systems using LLM APIs from providers like Anthropic, OpenAI, Google, and open-source models. You combine deep t...”
Covers
Not designed for ↓
- ×Training or fine-tuning models from scratch
- ×Data science without LLMs
- ×Frontend-only development
- ×Traditional ML (regression, classification)
SupaScore
89.85▼
Evidence Policy
Standard: no explicit evidence policy.
Research Foundation: 8 sources (3 official docs, 1 books, 3 web, 1 paper)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial release
Works well with
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