RAG and LangGraph for AI Engineers: Build Production Retrieval Pipelines
“Your Hand-Rolled RAG Is Lying. Build the Real Pipeline.”
Replace your hand-rolled prompt concatenation with a LangGraph state machine that retrieves, reranks, and generates with observable, production-grade quality metrics.
One-time · Lifetime access · Certificate included
- ✓6 modules of content
- ✓17 concept slides
- ✓18 practical exercises
- ✓24 quiz questions
- ✓Capstone project
- ✓LearnAspire certificate
Learning Outcomes
What you'll learn
The day after you finish
The day after completing this course, you will open your team's LangChain retrieval script, replace it with a LangGraph StateGraph that chains a Chroma retriever node → Cohere Rerank node → conditional edge router → GPT-4o generation node, add a no-result fallback branch, instrument the graph with Prometheus counters for retrieval latency and context token count, run pytest against five retrieval quality assertions, and open a pull request with a CHANGELOG.md — without asking a senior engineer to review the architecture first.
Who this is for
- Primary: ML Engineer or AI Engineer with 2–4 years experience who has built RAG prototypes with LangChain or raw OpenAI SDK but whose pipeline hallucinates, overflows the context window, or silently fails on no-result queries
- Secondary: Senior Software Engineer transitioning into LLM infrastructure who understands vector databases and embeddings but has never modelled retrieval as a stateful graph with conditional branching
- Tertiary: ML Platform Engineer or Tech Lead who will review, productionise, or set reliability standards for a team's RAG pipeline and needs to understand what observable, testable retrieval quality looks like
Prerequisites
- Python 3.10+ proficiency — you write decorators, dataclasses, and async functions without looking them up
- Hands-on experience calling an LLM API (OpenAI, Anthropic, or equivalent) and pulling documents from a vector store (Pinecone, Chroma, Weaviate, or equivalent)
- Conceptual understanding of transformer embeddings and cosine similarity — you do not need to implement them, but you must know why two chunks with similar embeddings can be factually unrelated
Curriculum
6 modules · full breakdown
Capstone Project
FinanceBot Production RAG Pipeline: Pull-Request-Ready LangGraph Deliverable
Build and submit a complete, deployable LangGraph 0.2.x retrieval pipeline against FinanceBot's 14,000-page compliance corpus. The pipeline must implement: a Chroma 0.5.x retriever node with a benchmarked recursive-character chunking config, a Cohere Rerank v3 node with retry logic and rate-limit handling, a conditional edge router with named branches for no-result, low-confidence, and context-overflow states, a GPT-4o generation node with citation-grounded prompt assembly, a Redis-backed checkpointer for multi-turn session state, and a Prometheus metrics endpoint emitting retrieval latency, rerank score delta, context token count, and citation match rate — all packaged as a FastAPI service with a pytest suite containing five retrieval quality assertions and a Grafana dashboard JSON export.
What you'll deliver
A GitHub-ready pull request containing: (1) langgraph_pipeline.py — the full StateGraph definition with typed state, all nodes, and all conditional edges; (2) eval/retrieval_eval.py — a pytest suite with five assertions covering precision@5, mean rerank score delta, context token budget, no-result branch activation rate, and citation match rate; (3) monitoring/grafana_dashboard.json — a Grafana dashboard export with panels for each Prometheus metric; (4) CHANGELOG.md — documenting every retrieval architecture decision with before/after precision@5 and mean context token count; and (5) requirements.txt pinned to exact versions matching the course tech stack