Sep 1, 2026
Build better RAG applications—before they ever reach production.
Creating a high-quality Retrieval-Augmented Generation (RAG) system isn’t as simple as connecting a knowledge base to an LLM. Every decision—from retrieval strategy and context size to prompt design and model selection—directly impacts answer accuracy, cost, and latency.
In this demo, you’ll see how Progress Agentic RAG helps you test, compare, and optimise your entire pipeline in a controlled environment before deployment.
What you’ll learn:
- How to experiment with different search configurations in a sandbox environment
- How to analyse answers with full transparency, including citations, token usage, and latency
- How to use Prompt Lab to compare multiple LLMs (GPT-4o, Claude, Gemini, and more) side-by-side
- How to use RAG Lab to test multiple retrieval strategies and configurations at once
- How to make data-driven decisions on model selection, cost, and performance
Why it matters:
Traditional RAG development involves guesswork—testing one configuration at a time and hoping for the best. With Agentic RAG, you can:
Validate outputs against your own data
Compare models and strategies instantly
Eliminate costly surprises in production
Key takeaway:
No guesswork. No blind deployments.
Test, compare, and validate every part of your RAG pipeline—all in one place.
Learn more: https://www.progress.com/agentic-rag
