LlamaIndex development for document-heavy AI

Where LlamaIndex fits

Most retrieval failures are not model failures. They happen earlier: a table was flattened into nonsense, a heading was separated from its paragraph, or two versions of the same policy were indexed side by side. LlamaIndex gives you good tools for that stage: readers for many file types, node parsers that respect document structure, metadata on every chunk, and several index and retriever types.

If your product is mainly about orchestrating actions across tools, a different framework may serve you better. We compare the two on our LangChain page. If your question is whether you need retrieval at all, start with RAG vs fine tuning.

Worked example: a policy manual with tables

Imagine a 400-page insurance policy manual in PDF, with benefit tables, footnotes and a revision table at the back. This is an invented example, but every issue in it is a common one.

Each fix is testable: we write questions whose correct answer depends on the fix, and check that the pipeline now gets them right.

What a build usually contains

We have no published case study on LlamaIndex, so this is the typical scope rather than a client story.

1. Document inventoryFile types, languages, update frequency, who may see what. This decides parsing tools and access rules.
2. Ingestion pipelineParse, clean, split, attach metadata, embed, store. Written as a rerunnable job so new files are picked up without a rebuild.
3. Retrieval tuningCompare chunk sizes, add keyword search alongside vectors, add a reranker if the first results are noisy.
4. Answer layerPrompts that require citations to file and page, and an explicit 'not found in the documents' response.
5. EvaluationA set of real questions with known source passages, scored for retrieval hit and answer faithfulness.

The user-facing side, whether a chat widget, an internal tool or an API, follows from RAG chatbot development.

Gotchas

• Re-indexing cost. Changing the embedding model means re-embedding everything. Decide early and keep the raw parsed text so you can rebuild without re-parsing.
• Permissions. If a manager may read a file and an intern may not, the filter must apply at retrieval, not after the answer is generated.
• Default chunk sizes. They are rarely right for your documents. Measure instead of copying a tutorial.
• Parsing services. Some hosted parsers send your files to a third party. We check where your documents go before we pick one.
• Citations that look right but are not. We verify that the quoted passage exists in the cited page.

How we judge whether it works

We write 60 to 100 questions with you before tuning anything. Each one has a known source page and an expected answer. Two scores matter: did the right passage come back in the top results, and did the final answer stay faithful to that passage. When a score drops after a change, we know which change caused it. The same set runs again at every release, and you can add new questions whenever users find a gap.

Timeline

Scanned archives and multilingual libraries add time at the first stage. We say so in the quote rather than discovering it later.