AI & Machine Learning💻 Technical CourseLearnAspire Certified

RAG Systems from Embeddings to Production — Build Search-Augmented LLM Apps

Name: RAG Systems from Embeddings to Production — Build Search-Augmented LLM Apps
Price: 2999.00 INR
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“Your LLM is only as accurate as your retrieval layer.”

The production engineering guide to building retrieval-augmented generation pipelines — from document ingestion to deployed search endpoint

Advanced12h6 modules45 slides18 exercises24 quiz Qs✓ Verified Apr 2026

🔥 Launch Price — 63% off. Limited time.

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✓6 modules of content
✓45 concept slides
✓18 practical exercises
✓24 quiz questions
✓Capstone project
✓LearnAspire certificate

Learning Outcomes

What you'll learn

→Build a document ingestion pipeline with chunking strategies tuned for retrieval precision — recursive character splitting, semantic chunking, and overlap windows — with measurable impact on retrieval recall

→Select and evaluate embedding models (OpenAI text-embedding-3, Cohere embed-v3, open-source alternatives) using retrieval benchmark metrics rather than defaults

→Implement and operate three vector store backends — FAISS for local development, Pinecone for managed production, pgvector for PostgreSQL-native deployments — with the trade-off model for choosing between them

→Build a retrieval pipeline with hybrid search (dense + sparse BM25), cross-encoder reranking, and MMR (Maximum Marginal Relevance) diversity scoring

→Handle production retrieval failures — empty results, ranking inversions, embedding cache staleness, and document freshness — with explicit fallback logic and circuit breaker patterns

→Deploy a production RAG endpoint (FastAPI + vector store + LLM) with sub-500ms p95 latency, retrieval hit-rate instrumentation, and an observability dashboard showing embedding cache performance and ranking quality metrics

The day after you finish

The day after completing this course, you will build a working document ingestion pipeline by implementing recursive character splitting with overlap windows on your own dataset and measuring its retrieval recall against your baseline. You will then evaluate at least two different embedding models using standard retrieval benchmarks to determine which one performs best for your specific use case before deploying your RAG system.

Who this is for

Primary: AI engineers and senior backend developers with 2–5 years of experience who have shipped at least one LLM-powered feature (chatbot, summarisation, function calling) and now need to add reliable domain-specific knowledge retrieval to their systems. They know Python, have called LLM APIs, but have not built a production RAG pipeline with engineered chunking, embedding model selection, reranking, or production observability.
Secondary: ML engineers who understand transformer models and have used embeddings conceptually but have not built the full retrieval pipeline that makes embeddings useful in production — the ingestion pipeline, the vector store architecture, the reranking layer, and the fallback handling.

Prerequisites

Proficiency in Python including async/await, type hints, and OOP
Hands-on experience with at least one LLM API (OpenAI, Anthropic, or Cohere) — function calling or structured output experience preferred
Working knowledge of transformer models and what an embedding vector represents (not required to train models, required to understand why cosine similarity works)
Experience with at least one database (SQL or NoSQL) — indexing, query planning, and performance concepts
Familiarity with FastAPI or equivalent async Python web framework
Basic understanding of retrieval metrics: precision, recall, NDCG

Curriculum

6 modules · full breakdown

🤖 Part of: AI Engineering Path

Step 1 — Foundations

→Step 2 — Core Skills

→Step 3 — RAG

→Step 4 — LangGraph RAG

→Step 5 — Agent Systems

→Step 6 — Production

→Step 7 — MCP

→Step 8 — Enterprise

← Previous: Step 2 — Core Skills Next in path: Step 4 — LangGraph RAG →

🏆

Capstone Project

Ship the Meridian Health Clinical RAG Service to Production

Maya hands you a 1,200-document corpus of internal clinical guidelines, pharmacy references, and case studies. The clinical informatics team needs a retrieval-augmented search service that returns accurate, citable answers in under 500ms p95 — and a retrieval metrics dashboard the on-call engineer can use to diagnose a bad query in 90 seconds. You inherit an empty repo. You apply every technique from Modules 1–5 — chunking strategy tuned for clinical documents, embedding model selection with hit-rate benchmarks, hybrid search with cross-encoder reranking, graceful empty-result handling — then deploy the final system as a FastAPI Docker container with Prometheus metrics and structured logging.

What you'll deliver

A deployed Meridian Health Clinical RAG Service — FastAPI endpoint inside a Docker container, backed by your choice of FAISS / Pinecone / pgvector, with hybrid search plus cross-encoder reranking. Shipped with: (1) a documented chunking-strategy decision memo, (2) an embedding-model benchmark report comparing at least two models on the actual Meridian corpus, (3) a retrieval regression test suite that CI runs on every push, (4) a Prometheus /metrics endpoint emitting retrieval hit rate, p95 latency, and empty-result rate, and (5) a one-page incident runbook for the three most common failure modes.

Portfolio value

A production RAG system integrating custom chunking, embedding selection, hybrid search with reranking, and FastAPI deployment with retrieval metrics dashboards, demonstrating mastery of retrieval optimization, vector database architecture, and the ability to architect search-augmented LLM applications from ingestion through production observability.