AI TRAINING
LlamaIndex for RAG-Heavy Applications
Build production-grade retrieval-augmented systems using LlamaIndex data connectors, indexes, and query engines.
What it covers
This hands-on technical programme covers LlamaIndex architecture end-to-end: data ingestion pipelines, vector and keyword indexes, query engine composition, and multi-document reasoning. Participants implement real retrieval-augmented generation systems, learn when LlamaIndex outperforms LangChain for document-heavy workloads, and leave with reusable code patterns for production deployment.
What you'll be able to do
- Build an end-to-end RAG pipeline using LlamaIndex data connectors and a vector store index from scratch
- Configure and compare at least three index types and justify the choice for a given retrieval use case
- Implement a multi-document SubQuestion query engine capable of synthesising answers across heterogeneous sources
- Evaluate retrieval quality using hit rate and faithfulness metrics and iterate on chunking and embedding strategies
- Deploy a LlamaIndex-powered query service with logging, caching, and token-cost controls
Topics covered
- LlamaIndex architecture: nodes, documents, and the indexing pipeline
- Data connectors and loaders for PDFs, databases, APIs, and web sources
- Vector store indexes vs. list indexes vs. tree indexes, trade-offs and selection
- Query engine composition and router query engines for multi-index retrieval
- Multi-document reasoning with SubQuestion and knowledge graph query engines
- Retrieval evaluation: hit rate, MRR, and faithfulness scoring
- LlamaIndex vs. LangChain: decision framework for RAG-heavy workloads
- Deploying LlamaIndex pipelines in production with observability and caching
Delivery
Delivered over 2–3 days either in-person or live-remote via video call with shared coding environment (JupyterHub or GitHub Codespaces). Each module follows a pattern of 20-minute concept walkthrough followed by 40-minute guided lab. Participants receive a private GitHub repo with starter notebooks, solution branches, and a capstone project brief. A shared Slack or Discord channel provides async support for up to four weeks post-training.
What makes it work
- Start with a real internal document corpus during training so labs are immediately relevant
- Instrument retrieval pipelines with evaluation metrics from day one, not as an afterthought
- Designate a technical owner post-training to maintain the LlamaIndex version and connector dependencies
- Pair the bootcamp with a follow-up architectural review two weeks after deployment
Common mistakes
- Using a flat list index for large corpora, causing slow and expensive full-scan queries
- Skipping retrieval evaluation, teams ship RAG systems without measuring retrieval quality, then blame the LLM
- Over-engineering with LangChain abstractions when LlamaIndex's native document store covers the use case more cleanly
- Ignoring chunk size and overlap tuning, leading to poor context windows and hallucinated summaries
When NOT to take this
This training is not the right fit for a team that has not yet chosen an LLM stack or is still evaluating whether RAG is the correct architecture, they need a broader LLM application design workshop first.
Example providers
This page describes a training topic. It gives no price or duration: those depend on the provider and your team. The free diagnostic tells you which trainings your team needs first.