Calling an LLM API and wrapping a UI around it gives you a demo. Shipping it to real users takes a full stack:

1. LLM: the intelligence layer
2. Data extraction: Firecrawl, Crawl4AI, Docling, LlamaParse
3. Embeddings: text into vectors
4. Vector database: Pinecone, Qdrant, Weaviate, Milvus, PostgreSQL
5. RAG and orchestration: retrieve, build context, send to the LLM
6. Application: agents, copilots, search, workflows
7. Evaluation: Ragas, TruLens, Giskard
8. Production: auth, observability, rate limits, caching, monitoring, versioning, cost control

The real flow is not User → LLM → Answer.

It is Data → Extraction → Embeddings → Retrieval → Context → LLM → Application → Evaluation → Production.

LLM = Intelligence.
Full Stack = Product.

What does your production AI stack look like? Which layer gave you the most trouble?

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