Software development
LLM Application Development: Architecture, RAG and Costs
A practical guide to building LLM applications: architecture choices, retrieval-augmented generation, evaluation, costs and common pitfalls.
By Intelvue Engineering · Updated · 2 min read
The short answer: most successful LLM applications are not a model plus a prompt. They are a small system: retrieval that finds the right information, a model that reasons over it, guardrails that keep outputs safe, and an evaluation loop that proves it works. Get those four right and the choice of model becomes an implementation detail.
What is an LLM application?
An LLM application uses a large language model to understand and generate text inside a product or workflow: a support assistant, a contract analyser, a sales email drafter, an internal knowledge search. The model provides language skill; your application provides context, rules and access to data.
The core architecture
| Layer | Purpose | Typical choices |
|---|---|---|
| Interface | Where users interact | Chat, form, API, background job |
| Orchestration | Builds prompts, calls tools, manages steps | Application code or an agent framework |
| Retrieval | Finds relevant information | Vector search, keyword search, hybrid |
| Model | Understands and generates | Commercial or open models, chosen per task |
| Guardrails | Keeps outputs safe and on-topic | Input checks, output validation, policies |
| Evaluation and monitoring | Measures quality over time | Test sets, logging, human review |
What is retrieval-augmented generation (RAG)?
RAG means fetching relevant documents first and giving them to the model as context, so answers are based on your data rather than the model's memory. It keeps answers current without retraining and lets you show sources.
Good RAG depends on boring details: splitting documents sensibly, keeping metadata, combining keyword and semantic search, and re-ranking results. Most quality problems in LLM apps are retrieval problems.
How do you evaluate an LLM app?
Build a test set of real questions with known good answers before you build the app. Then measure:
- Correctness: is the answer right?
- Groundedness: is it supported by the retrieved sources?
- Refusals: does it decline when it should?
- Latency and cost: per request, at expected volume.
Run the set on every change. Without it, you are tuning by anecdote.
What does an LLM app cost?
Three buckets: building the system (integration, retrieval, guardrails, evaluation), model usage (priced by the amount of text processed), and ongoing operation (monitoring and content updates). Smart routing, sending easy requests to smaller models, and caching repeated work keep usage costs down.
Common pitfalls
- Starting with fine-tuning before trying retrieval and good instructions.
- No evaluation set, so nobody can tell if a change helped.
- Sending sensitive data to providers without checking their data-use terms.
- Treating the demo as the product. Production needs logging, rate limits, fallbacks and monitoring.
Build it with people who ship
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Frequently asked questions
- Do we need to fine-tune a model?
- Usually not at first. Retrieval-augmented generation plus good instructions solves most business use cases. Fine-tuning helps for very specific formats or tone once you have good data.
- Which LLM should we use?
- Pick per task. Larger models reason better; smaller ones are cheaper and faster. Many production systems route simple requests to small models and hard ones to larger models.
- How do we stop hallucinations?
- Ground answers in retrieved sources, ask the model to cite them, refuse when sources are missing, and test against an evaluation set before every release.