Скачать или смотреть EP 1 • E-commerce Product Copy with LangGraph | Prompt-Chaining

Episode 1 — Prompt Chaining with LangGraph

In the first “real” episode of AI Workflows with LangGraph we take Anthropic’s Prompt-Chaining pattern and turn it into a working automation that writes rich product descriptions for any e-commerce shop — starting from a product ID, image URL and a few specs.

🔑 What you’ll learn
The 3 core LangGraph concepts: State → Nodes → Edges
How to split a linear task into simple LLM steps for higher output quality
Using conditional edges (the graph-equivalent of if/else)
Debugging & visualizing your workflow in LangGraph Studio
Trade-offs: better accuracy vs. longer latency

🛠 Resources
Companion code:
https://github.com/kurkowski93/ai-wor...

Anthropic blog post (patterns inspiration):
https://www.anthropic.com/index/build...

LangGraph docs:
https://python.langchain.com/docs/lan...


⏰ Chapters
00:00 Series kickoff & today’s goal
00:31 What is Prompt Chaining?
01:25 Why simple sub-tasks boost LLM accuracy
02:23 LangGraph crash-course: State, Nodes, Edges
02:59 Designing the e-commerce workflow (diagram)
03:30 Defining the State schema
04:09 Node 1 – find_product_details()
05:00 Node 2 – extract_features_from_image()
05:45 Node 3-7 – generate description, SEO title, keywords …
06:55 Conditional edge: stop if product not found
07:50 Building & compiling the graph
08:46 Live demo in LangGraph Studio
09:43 Reviewing outputs & practical next steps
10:33 What’s next? Faster runtimes with Parallelization
10:50 Like 👍, subscribe & grab the repo

🚀 Next episode
We’ll optimize today’s workflow with Parallelization to slash latency without sacrificing quality.

Watch · Clone · Build smarter AI.

#LangGraph #PromptChaining #AIWorkflows #LLM #Ecommerce