Course · Advanced
Advanced RAG
A working RAG pipeline is only the starting point. Real deployments face adversarial inputs, retrieval that quietly misses, answer quality that drifts over time, and cost that has to be justified. This course takes the RAG pipeline you can already build and makes it production-ready. You'll defend against prompt injection and unsafe instructions, add a self-RAG loop that judges its own answers and retries when evidence is thin, monitor reliability and cost against thresholds, and assemble everything into an extensible harness you can operate and extend.
- Advanced friendly
- 8 hrs
- 4 lessons
- Premium
Course overview
Take a working RAG pipeline into production, covering prompt injection defense, self-RAG with autonomous evaluation, production monitoring and reliability, and assembling an extensible production harness.
What's inside
4 lessons
- 01 120 minRAG Pitfalls, Security, and Prompt Injection Defense
Learn RAG security by detecting prompt injections, adding source-trust metadata, filtering malicious content, and building defenses against attacks targeting retrieval-augmented generation pipelines.
- 02 120 minSelf-RAG and Autonomous Evaluation
Teach GitQuest to judge its own answers and retrieve again when the first retrieval missed the mark, using a relevance dimension that breaks the evidence-only rule on purpose.
- 03 120 minProduction Monitoring and Reliability
Harden GitQuest's monitoring for production by tightening the alert thresholds, treating an unmeasured metric as an alert of its own, and comparing runs across models, across time, and across slices.
- 04 120 minGuided Project: Production RAG Application
Take the GitQuest pipeline you have built across this course and wire it into one production harness, with a case-handler registry that grows without edits to the orchestrator, a cost-per-correct-answer metric, and canary runs that catch drift before a config change ships.
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