RAG is easiest to reason about when retrieval is treated as a separate evidence-selection step with its own quality contract.
Query construction
Clarify what evidence is actually needed before selecting embeddings, rewriting, filtering, or metadata constraints.
Candidate selection
Retrieve candidates with explicit freshness, access, latency, and relevance expectations.
Grounded generation
Pass relevant and appropriately formatted context to the generator. More context is not automatically better context.
Evaluation boundary
Test retrieval and generation separately where possible because a wrong answer may originate in missing evidence, poor ranking, or generation behavior.
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
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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.