How to Evaluate a Retrieval-Augmented Generation System
A RAG assistant can fail at retrieval, at reasoning or at answering. Evaluating each stage separately is the fastest way to improve quality.
Insight topic
Generative AI insights
Retrieval, grounding, evaluation and deployment of generative AI and language models. Articles in this topic are written for leaders and practitioners who need practical, evidence-based guidance.
2 articles in Generative AI
What to Monitor After an LLM Application Goes Live
Uptime dashboards will not tell you that answers have quietly become worse. A monitoring plan for quality, grounding, safety, cost and user behaviour.
Topic guide
Understanding generative ai
Generative AI and large language models draft, summarise, explain and transform content. The articles in this topic address the engineering behind dependable generative AI: grounding in approved knowledge, evaluation, security and cost control.
FAQ
Key questions about generative ai
How can generative AI answers be made reliable?
Ground answers in retrieved, approved sources, require citations, allow the system to decline when evidence is missing and evaluate quality continuously on real questions.
What is the most common generative AI architecture in business?
Retrieval-augmented generation, where relevant documents are retrieved and supplied to a language model that writes an answer based on them.
Should organisations fine-tune language models?
Usually not first. Retrieval and good prompting meet most needs. Fine-tuning helps for specialised formats, tone or narrow tasks where evaluation shows a benefit.
Related
Services related to generative ai
Where these ideas are put into practice.
Next step
Discuss generative ai with our team
If you are working through a related challenge, we would be glad to talk it through.
Talk to Our Technology Team
Or write to care@aarhit.com