LlamaIndex RAG: Building retrieval systems

RAG looks simple on a whiteboard: search a knowledge base, then let the model write. In practice, most failures trace back to weak retrieval or sloppy ingestion, not the LLM. The answer quality lives or dies on what gets fetched and how that context is packaged.

This guide cuts out the fluff and shows how to make RAG durable. Expect concrete knobs to turn, common traps to avoid, and a test loop that actually improves results over time.

Understanding the fundamentals of retrieval-augmented generation

Think of RAG as two tight loops that must stay in sync: retrieve relevant context, then generate a grounded answer. The Pragmatic Engineer breaks down the practical pipeline nicely, from ingestion to ranking to response orchestration RAG overview. The core idea is simple: better candidates in, better answers out.

A minimal RAG flow looks like this:

• Parse and normalize documents; split into chunks; embed; index.

• Take a user query; retrieve top-k chunks; optionally re-rank; pass the best context into the model.

• Generate an answer that leans on those sources and cites them.

Small choices compound. Chunk size, overlap, reranking, and query reformulation have an outsized effect on accuracy, as the LlamaIndex team calls out in their advanced playbook cheat sheet. Hybrid retrieval tends to help once text gets messy or formats vary; the OpenSearch walkthrough shows a pragmatic pattern for mixing lexical and vector search from PDFs to intelligent answers.

On the ingestion side, LlamaIndex represents documents as nodes, then splits them into manageable chunks. For a quick skim of the basics, the community’s “naive RAG” example is a useful starter reference naive RAG example. A simple, consistent pipeline sets retrieval up for success.

Building an efficient ingestion pipeline from scratch

Ingestion sets the ceiling on recall, latency, and cost. Get it right early. A typical pipeline:

1. Load raw sources; extract text; normalize formatting.

2. Split into chunks; attach metadata; embed; index.

Now tune it. Chunk size is not one-size-fits-all. The LlamaIndex team suggests starting ranges that work in practice: 400–800 tokens for prose; 80–160 for code cheat sheet. Keep overlap small but nonzero so concepts carry across boundaries. Measure index throughput and retrieval hit rates; when recall stays high, prefer fewer, denser chunks to reduce cost.

Here’s how to reduce index overhead without hurting recall:

• Batch inserts and commit after N nodes per source to avoid churn.

• Strip boilerplate like nav menus, headers, and legal footers before embedding.

• Budget tokens up front using the Pragmatic Engineer’s planning frames RAG overview.

Structured data deserves dedicated treatment. Add connectors for tables, APIs, and events; normalize entities into a simple schema and map fields to nodes. The LinkedIn guide shows a clean first pass with LlamaIndex that you can borrow and extend build your first RAG system using LlamaIndex. For quick prototyping, the community’s starter walkthroughs are handy scaffolding before production hardening starter guides.

Routing matters too. Use field‑aware retrievers so titles boost for docs, function names boost for code, and product areas or versions act as filters. LlamaIndex query engines make this routing practical; the naive example still helps as a mental model for wiring naive RAG example.

Advanced techniques for improving retrieval accuracy

Once chunking is stable, tighten what reaches the model. Start with re‑ranking: apply a cross‑encoder on the top-k candidates or use MMR to balance relevance and diversity. The LlamaIndex cheat sheet outlines both options and when to prefer each cheat sheet. Expect immediate gains in grounding.

Hybrid retrieval lifts recall across uneven content. A practical pattern from the OpenSearch tutorial mixes lexical and semantic signals, then re‑ranks for intent fit from PDFs to intelligent answers. A few recipes:

• BM25 + embeddings: fetch with keywords; re‑rank by vector similarity.

• Vectors + metadata filters: narrow scope first; re‑rank the remainder.

• Query rewrite then retrieve: expand terms or synonyms; re‑rank to curb drift.

Practical knobs to actually tune:

• Top‑k at retrieval: 20–100 for prose; 50–200 for code or logs.

• Re‑rank depth: re‑score the top 50 and keep the top 5–10.

• Query reformulation: add entities, synonyms, and constraints when user queries are short or vague.

The why is simple: stronger candidates mean safer answers. The Pragmatic Engineer’s overview compares these tradeoffs and is a good gut check when deciding what to try first RAG overview.

Strategies for evaluating RAG workflows

No pipeline ships without a harness. Start with small, labeled sets and a few crisp metrics. LlamaIndex’s advanced patterns suggest focusing early on context overlap and faithfulness: how much of the answer is grounded in provided sources, and how strictly it sticks to them cheat sheet.

Add retrieval and latency metrics next:

• Precision@k and Recall@k by query class.

• Context relevancy and answer relevancy scored by a judge model or humans.

• End‑to‑end latency, token usage, and cost per answer.

Tie metrics to real use cases. The Pragmatic Engineer’s writeup offers a practical way to frame queries by intent and difficulty RAG overview. The LinkedIn starter shows how to set baselines before piling on complexity build your first RAG system using LlamaIndex.

Here’s what typically goes wrong:

• Low context overlap: chunking is off or candidates are too narrow. Increase chunk size or adopt hybrid search as in the OpenSearch guide from PDFs to intelligent answers.

• Faithfulness dips: tighten prompts, enforce citations, or add a re‑rank stage, which the LlamaIndex team highlights as a fast win cheat sheet.

Once offline metrics look sane, validate changes with guardrails in production. Teams often run A/B tests on retrievers, re‑rankers, and prompts, measuring goal rates and cost per successful answer. A product like Statsig makes it straightforward to roll out changes safely and see real impact without guessing. Use holdouts, long‑running experiments, and feature flags to separate signal from noise.

Closing thoughts

RAG works when retrieval and generation move in lockstep. Get ingestion right, feed the model stronger candidates with re‑ranking and hybrid search, and keep a tight evaluation loop that rewards faithfulness over flash. Small, boring choices create durable gains.

For more hands‑on detail, the Pragmatic Engineer’s overview is a solid map RAG overview, the LlamaIndex team’s playbook is full of practical recipes cheat sheet, and the OpenSearch walkthrough shows hybrid retrieval end to end from PDFs to intelligent answers. For a quick start, the community’s naive build is a good stepping stone naive RAG example, and the LLM learning thread has a simple tutorial flow starter guides. When you’re ready to ship, use Statsig to measure changes in the real world, not just in a notebook.

Hope you find this useful!