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Building Agentic RAG with LlamaIndexvsBuilding and Evaluating Advanced RAG Applications

Side-by-side comparison on level, duration, pricing, instructor, tier. Editor verdict on which course wins for which buyer.

DL.AI
DeepLearning.AI

Building Agentic RAG with LlamaIndex

The follow-up to Building and Evaluating Advanced RAG. Where the earlier course covered retrieval-engineering fundamentals, this one introduces agentic RAG — the loop where the agent decides what to retrieve, evaluates the result, and re-queries if needed. Production-grade pattern that solves the 'hallucination on retrieved context' problem most basic RAG apps have. Free, taught by Jerry Liu, the LlamaIndex co-founder.

DL.AI
DeepLearning.AI

Building and Evaluating Advanced RAG Applications

The right course for the moment a 'just stuff it into the context window' RAG starts failing. Covers retrieval evaluation, sentence-window retrieval, auto-merging, and the production-failure modes that toy RAG demos don't surface. Free, taught by the LlamaIndex founder + the team behind TruLens evaluation. The pre-req: you should already have built a basic RAG and watched it answer questions wrong. If you haven't, start with a simpler intro.

Side-by-side

DimensionBuilding Agentic RAG with LlamaIndexBuilding and Evaluating Advanced RAG Applications
ProviderDeepLearning.AIDeepLearning.AI
Editorial tierHands-on reviewedHands-on reviewed
LevelAdvancedAdvanced
Formatself pacedself paced
Duration~2 hours (4 lessons)~1.5 hours (5 lessons)
PricingFreeFree
InstructorJerry Liu — Co-founder, LlamaIndexJerry Liu & Anupam Datta — Founder, LlamaIndex; Co-Founder, TruEra
RatingNo public ratingNo public rating
Topicsrag systems, build ai agents, ai evalsrag systems, ai evals
Last verified2026-05-242026-05-23

Pros & cons

Building Agentic RAG with LlamaIndex
Pros
  • +Solves a real production-RAG failure mode (single-shot retrieval misses context)
  • +Free, by LlamaIndex co-founder
  • +Material follow-up to the basic-RAG course
Cons
  • −Strong pre-reqs: basic RAG + LlamaIndex familiarity assumed
  • −LlamaIndex-flavored — patterns transfer but examples are vendor-specific
Building and Evaluating Advanced RAG Applications
Pros
  • +Production-grade content — failure modes, not just happy paths
  • +Free, 90 minutes, by LlamaIndex + TruEra principals
  • +Evaluation is the under-covered topic in the RAG space
Cons
  • −Assumes you already understand basic retrieval
  • −LlamaIndex-flavored — if you're a LangChain shop, mental translation needed

Which course is for whom?

Building Agentic RAG with LlamaIndex
Best for
  • · Engineers whose basic RAG works in dev but fails in prod
  • · Anyone building research agents (Perplexity-style)
Not ideal for
  • · First-time RAG builders — take Building and Evaluating Advanced RAG first
Building and Evaluating Advanced RAG Applications
Best for
  • · Engineers whose basic RAG works in dev but fails in prod
  • · AI engineers debugging hallucinations on retrieved context
Not ideal for
  • · People building their first RAG — start simpler
  • · Anyone allergic to LlamaIndex idioms

Editor's short verdict

Both cover the same topic at the same level; pick by format and pricing. Building Agentic RAG with LlamaIndex (self paced, Free) vs Building and Evaluating Advanced RAG Applications (self paced, Free). If price-sensitive, take the cheaper; if commitment-sensitive, take the cohort or paid option for the accountability.

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Building Agentic RAG with LlamaIndex vs Building and Evaluating Advanced RAG Applications (2026): which course wins? · AI Agent Rank