Outsource LLM & RAG Development the Right Way

Retrieval-augmented generation is deceptively easy to demo and genuinely hard to ship. Outsourcing LLM and RAG development succeeds only when the partner treats it as production engineering. Here is what that means.

What production RAG actually requires

• Quality retrieval — chunking, embeddings, and search that surface the right context.
• Grounding and citation — answers tied to sources, reducing hallucination.
• Evaluation — measuring answer quality against real questions.
• Guardrails and monitoring — safe behavior, observed over time.

This is the AI enablement work an FDE carries into adoption—see forward deployed engineers and agentic AI.

How to vet a RAG partner

Ask about	Look for
A shipped retrieval system	Production, not a demo
Data quality handling	Chunking, cleaning, structure
Evaluation	Metrics tied to real questions
Hallucination control	Grounding and citation

Data and security first

RAG runs on your data, so align data handling and security in discovery before any build—see offshore AI data security.

Scope before you compare

An LLM app's cost depends heavily on data complexity and evaluation needs—scope the outcome first. See how to estimate an AI project cost.

Why FISTA

FISTA Solutions builds production LLM and RAG systems as part of its AI enablement practice—US-registered, offshore delivery, security aligned in discovery, and a verified record of 150+ projects across 12+ countries.

Need a production RAG system, not a demo? Talk to FISTA, or explore hiring AI agent developers.