Tutorials Prompt Engineering Tutorial
Production RAG Architecture — Complete Guide
Production RAG Architecture — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 50 of 100
Production RAG Architecture
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 5: RAG Systems
What is this?
Production RAG spans ingest pipeline, vector index, query service, prompt builder, LLM, cache, and observability — not a notebook demo.
Why should you care?
PromptVerse RAG stack runs on K8s with separate ingest workers and query API autoscaling.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
ingest_queue → chunk/embed → vector_index
query_api → auth → retrieve → prompt_build → llm → validate_citations → response
metrics: retrieval_latency, faithfulness_score, p95_tokensWhat happened?
- Async ingest decouples from query path.
- faithfulness_score evals answers against sources in shadow traffic.
Practice next
- Draw boxes for ingest vs query path.
- Add metric per box.
- Define SLO p95 query < 3s.
- Blue-green index swap.
- Cache retrieve results by query hash 5min.
Remember
Separate ingest and query. Observability on each stage. Eval faithfulness continuously.
Black Friday traffic
Query QPS 10× normal.
Outcome: Query API scales; ingest lag does not block search.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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