---
title: How long does it take to build a production LLM application?
source: https://rubradigital.com/answers/how-long-does-it-take-to-build-an-llm-application
site: Rubra Digital
description: Realistic timelines for LLM and RAG projects: prototype, production build and the gap between them, plus what actually causes delay.
updated: 2026-05-30
topic: platform
also_asked: Why does the last 20% of an AI project take so long? | How fast can we get an AI pilot live? | Why did our AI pilot never reach production?
---

# How long does it take to build a production LLM application?

**Summary:** A working prototype takes two to three weeks. A production system takes eight to sixteen weeks, and the difference is not polish. It is document parsing, evaluation, access control, observability and the failure handling that a demo never needs. The largest single cause of overrun is document quality discovered late: scanned PDFs, table-heavy files and inconsistent formats routinely double the ingestion effort. Teams that build a labelled evaluation set in week two finish faster than those that skip it, because every subsequent decision stops being guesswork.

## The two-week demo and the four-month system

You can build something impressive over a corpus of clean documents in two
weeks. It will answer questions well, demo beautifully, and create an
expectation that production is a fortnight away.

It is not, and the reason is worth being precise about. The demo works because
it was run on the good documents, by people who knew what to ask, with no
permissions, no logging, no error handling and no measurement. Production has to
work on the whole corpus, for people who ask badly, under access control, with
evidence that it works.

## Where the time actually goes

For a typical 12-week production build:

**Weeks 1 to 2. Corpus reality check and evaluation set.** What the documents
really look like, and a labelled set of real questions with known-correct
passages. Skipping this is the most expensive shortcut available.

**Weeks 3 to 5. Ingestion and parsing.** Consistently the largest and most
underestimated block. Scanned pages need OCR. Tables need structural extraction.
Documents exported from legacy systems arrive with the structure stripped out.

**Weeks 5 to 8. Retrieval engineering.** Chunking, embedding choice, hybrid
search weighting and metadata filters, all run as experiments against the
labelled set rather than as preferences.

**Weeks 7 to 10. Application layer.** Generation with citations and refusal
behaviour, conversation handling, permission enforcement at retrieval time, the
interface.

**Weeks 9 to 12. Hardening and handover.** Observability, cost controls, rate
limiting, failure paths, runbook, load testing, and the sessions that leave your
team able to change it.

The overlaps are intentional; these tracks run in parallel.

## What causes overrun

1. **Document quality found late.** Knowable in week one if anyone samples the
   corpus properly. Frequently is not.
2. **Permission requirements found late.** "Everyone can see everything" becomes
   "actually, four access tiers and regional restrictions" in week eight, and
   that reshapes retrieval.
3. **No evaluation set.** Without one, retrieval tuning becomes an endless
   subjective argument nobody can close.
4. **Scope drift after the demo.** The demo is so encouraging that three more
   departments want their corpus in it.
5. **Waiting on source system access.** Nearly always the long pole, and nearly
   always started too late. Begin the access requests in week one.

## Compressing it honestly

You can go faster by narrowing scope, not by skipping stages. One corpus, one
user group, one clearly defined question type, shipped in eight weeks, beats
four corpora shipped in twenty-four, and it produces the evidence you need to
justify the next phase.

The stages that look optional are the ones that determine whether the system
survives its first month with real users.

---

Source: https://rubradigital.com/answers/how-long-does-it-take-to-build-an-llm-application
Rubra Digital. Independent LLM and RAG consulting for regulated and document-heavy organisations in Europe and North America.
Contact: hello@rubradigital.com