Learning Objectives & Key Takeaways
0 / 4 Completed- Understand Agentic AI & LangChain core architecture and runtime execution model
- Audit real-world latency, throughput benchmarks, and memory footprint
- Implement zero-downtime deployment patterns with resilience guards
- Verify code snippets and architect blueprints against production SLOs
- Naive single-pass retrieval fails on multi-part questions because similarity search ignores logical structure
- Query decomposition plus cross-encoder re-ranking lifted recall at 5 from 61 percent to 84 percent on real tickets
- A LangGraph grading loop catches bad retrieval before generation, capped by a retry counter to control cost
The Support Bot That Confidently Lied at 2 AM
An on-call engineer at a fintech startup got paged because a customer screenshot was circulating on social media showing their support chatbot promising a refund policy that did not exist in any document the company had ever published.
The root cause was almost boring. The question asked was a compound one, something like what is the refund window for annual plans and does it change if I upgrade mid cycle, and the retriever pulled four chunks about the refund window and zero about upgrades.
- Why Single Shot Retrieval Falls Apart on Real Questions
- Query Planning: Teach the Retriever to Ask Better Questions
- Re-ranking: Fixing Recalls Precision Problem
- Self-Correction: Grading Documents Before You Trust Them
- The Trade-offs Nobody Puts in the Demo
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