Separate retrieval evaluation from generation evaluation
← Case Studies
AI Engineering
Taking a RAG system from demo to production
A representative AI-engineering scenario for a RAG prototype that produces promising demos but lacks measurable retrieval quality, evaluation and production controls.
RETRIEVE → EVALUATE → GUARD → OPERATE
Representative engineering scenario. It illustrates the type of problem-solving approach GNU Group can provide; it is not presented as a named-client testimonial or a claim of specific measured results.
Situation
Start with the observable problem.
A RAG application answers questions, yet quality varies across documents and users. The team cannot clearly attribute failures to chunking, embeddings, retrieval, reranking, prompt construction or model behavior.
Environment
LLM application
Vector search
Embeddings
Document ingestion
RAG orchestration
Signals to investigate
Inconsistent answer quality
Weak source attribution
No retrieval-quality baseline
Prompt changes have unpredictable effects
Cost / latency trade-offs are unclear
Engineering approach
Turn uncertainty into a sequence of testable decisions.
Build representative test sets
Measure chunking and retrieval behavior
Review trust boundaries and tool/data access
Define observability and production acceptance criteria
Typical deliverables
RAG architecture review
Evaluation plan
Retrieval findings
Security / guardrail recommendations
Production-readiness roadmap
Intended outcomes
A measurable quality baseline
Clearer diagnosis of RAG failure modes
Better separation of retrieval and generation concerns
A safer path from prototype to production
CONNECTED PATHS
Move from scenario to solution, training and technical guidance.
Explore Knowledge HubConsultingAI Engineering
AI Engineering
Move AI, LLM, RAG and agentic applications from prototype behavior toward measurable, secure and operable production systems.
ContinueTrainingAI Engineering
AI Engineering
Move beyond AI demos into engineered systems: machine-learning fundamentals, LLM applications, retrieval, agents, evaluation, observability, security and production deployment.
ContinueArticleAI Engineering
RAG in Production: Retrieval Is an Engineering System
Why production RAG depends on ingestion, chunking, retrieval, evaluation, security and observability—not only an LLM prompt.
ContinueTrainingSecurity
Security Engineering
Build security understanding across Linux, networks, applications, DevSecOps, containers, Kubernetes and AI systems with practical engineering controls.
ContinueConsultingAI Engineering
Security Engineering
Engineering-focused security assessment and hardening across Linux, delivery pipelines, containers, Kubernetes, applications, APIs and AI systems.
ContinueArticleAI Engineering
Linux Performance Troubleshooting: A Layered Method
A practical workflow for moving from symptoms to evidence across CPU, memory, storage, network and application layers.
ContinueHave a similar problem?