I Built a Practical Guide to LLM Engineering: RAG, Retrieval, Rerankers, and Evaluation

If you’re building LLM apps and feel confused about when to use keyword search, embeddings, rerankers, or vector databases, this repo is for that.

I built a docs-first repo on practical LLM system design patterns, covering pre-filtering, hybrid retrieval, rerankers, in-memory scoring vs vector DBs, batching, cleanup, and LLM-as-judge evaluation, with simple Python examples.

From my experience, embedding quality or RAG alone is rarely the full answer. The engineering harness around the LLM usually matters just as much as the model itself when building a real business solution.

The goal is to make this useful for both newcomers and working developers who want a clearer mental model for building reliable LLM systems.

Repo: https://github.com/SaqlainXoas/llm-system-patterns

I’d love feedback on it. If you find it useful, feel free to star the repo as well. I’d also be interested to hear your own engineering findings around retrieval, embeddings, reranking, RAG, evaluation, and where these approaches work or break in practice.