RAG combines a retrieval step that selects evidence with a generation step that uses that evidence to produce an answer.
Why retrieval is separate
The generator cannot use evidence it never receives, so retrieval quality is its own engineering problem.
Typical flow
A query is represented, candidates are retrieved, context is assembled, and a language model generates using that context.
Evaluation
Retrieval recall, grounding quality, evidence alignment, latency, and final task completion can all matter.
Portfolio connection
RAG and agent systems form part of the documented technical identity and deeper expertise pages.
This page is part of Abdullah’s technical knowledge library: a set of specific, crawlable resources that connect a search question to practical engineering evidence.
When the topic overlaps with Abdullah’s documented work, the links below provide deeper project or expertise context without turning general guidance into a personal credential.
Related work and reading
AI Agents & RAG
Continue into the most relevant project, expertise hub, article, or company context.
RAG as a Retrieval Boundary
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RAG System Design
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AI Developer / ML Engineer building end-to-end AI systems from research to production, with a focus on multimodal AI, LLM applications, retrieval, MLOps, and systems engineering. He is based in Rawalpindi, Pakistan and is the founder of GROVE SYSTEMS.