Why RAG demos fail in production
Understand the gap between a successful prototype and a system that can support real users, real data and changing requirements.
Free 90-Minute Live Online Session
RAG Beyond the Demo
An Engineering Perspective for Software Developers
Discover what it takes to move Retrieval-Augmented Generation beyond a working prototype and towards a reliable, maintainable and production-ready software system.
Places are limited. Joining instructions will be sent to registered attendees by email.
Why This Session?
A RAG demo is not yet a RAG system
Many Retrieval-Augmented Generation tutorials demonstrate a simple sequence:
Documents
Embeddings
Vector Search
LLM Response
That is useful for learning the basic mechanism, but it leaves many important engineering questions unanswered.
How do you know whether retrieval is working well? What happens when relevant information is missing, outdated or conflicting? How do you observe failures, control costs, protect sensitive data and improve the system over time?
The challenge is not simply getting an LLM to answer one question.
The challenge is engineering a system that can answer many questions reliably as documents, users and requirements evolve.
Session Outcomes
What you’ll learn
The session will introduce the principal engineering concerns involved in designing and operating production-oriented RAG applications.
Production RAG architecture
Explore the main components and responsibility boundaries of a RAG system, from ingestion and retrieval to generation and monitoring.
Retrieval quality
Examine why similarity search alone is not enough and how chunking, metadata, ranking and query handling affect results.
Evaluation
Learn how retrieval and generated answers can be evaluated independently rather than relying only on subjective inspection.
Observability and failure analysis
Identify what should be logged, measured and traced so that poor answers can be investigated and improved systematically.
Reliability and evolution
Consider guardrails, maintainability, security, cost control and the architectural choices that make future change safer.
90-Minute Live Session
What we’ll cover
15 minutes
Part 1 — Beyond the basic RAG pipeline
A brief review of the standard RAG workflow and the production concerns that are commonly omitted from tutorials.
25 minutes
Part 2 — Architecture of a production RAG system
A system-level view of ingestion, document processing, retrieval, generation, evaluation and operational services.
25 minutes
Part 3 — Retrieval and evaluation
Practical examples showing how retrieval decisions affect answer quality and how those decisions can be evaluated.
15 minutes
Part 4 — Reliability, observability and evolution
How architecture supports monitoring, troubleshooting, maintainability, security and controlled system improvement.
10 minutes
Questions and discussion
An opportunity to discuss practical RAG engineering concerns and questions submitted by attendees.
The session includes demonstrations and architectural discussion. Attendees will not be expected to code during the live session.
Audience
Who this session is for
Software Developers
Developers who want to understand how RAG applications should be structured beyond notebooks and proof-of-concept demos.
Software Architects and Technical Leads
Practitioners responsible for system boundaries, technology choices, reliability and long-term maintainability.
AI and LLM Engineers
Engineers building retrieval-based LLM applications who want stronger software engineering and architectural foundations.
Experienced Engineers Moving into AI
Software professionals applying established engineering principles to modern AI and LLM-based systems.
An Engineering Perspective
Production AI still requires software engineering
AI components do not remove the need for architecture, separation of concerns, testability, observability and controlled deployment. Their probabilistic behaviour makes those disciplines even more important.
This session treats RAG not as a prompt technique or isolated model feature, but as a complete software system that must be designed to operate, fail, recover and evolve.
Your Presenter
Dr Ahmad H. Estabrag
Software Architect · AI Engineer · Former University Lecturer
Ahmad has a PhD in Software Engineering and more than 30 years of experience across software development, software architecture, enterprise systems, consulting, research and technical education.
He spent eight years as a university lecturer and researcher, teaching software engineering and neural networks and supervising postgraduate research.
His current work focuses on production-ready AI systems, particularly LLM systems, Retrieval-Augmented Generation, agentic systems and computer vision.
Thursday, 26 November 2026 · 6:30–8:00 pm BST
Reserve your place
Join this free live session and explore Retrieval-Augmented Generation from a software engineering and architectural perspective.
Register Free
Confirmation and joining instructions will be sent by email.