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Build your first LLM app: a practical path for developers

By TechlyUpUpdated 2 min readDevelopers new to LLMs

Quick answer

Start with one narrow task, call a model API with a clear system instruction and structured output, and build a small test set before adding features. Handle errors, timeouts, and cost from day one, log inputs and outputs (without sensitive data), and only then add retrieval or tools. A small, reliable feature teaches more than an ambitious demo.

Pick a narrow, checkable task

Good first tasks have clear success criteria: classify support tickets, extract fields from invoices, summarise a document into a fixed schema. Avoid open-ended chat as a first project — it's hard to test.

Structure the call

Separate instructions, input, and output format.

System: You classify support tickets into one of: billing, bug, how_to, account, other. Reply with JSON only: {"category": ..., "confidence": "high|low", "reason": ...}.
User: <ticket>{ticket_text}</ticket>

Validate the JSON in code; on failure, retry once, then route to a human.

Test before you build more

A small evaluation set catches regressions early.

  1. Collect 30–50 realistic inputs with expected outputs.
  2. Include edge cases: empty, very long, multilingual, ambiguous.
  3. Score results automatically where possible.
  4. Re-run the set after every prompt or model change.

Production basics

Add timeouts, retries with backoff, rate-limit handling, cost tracking, and logging that excludes personal data. Treat model output as untrusted input to the rest of your system.

Common first-app mistakes

Almost every first LLM app hits some of these.

  1. Putting the API key in front-end code or committing it to a repository.
  2. Parsing free-text output with regular expressions instead of requesting structured output.
  3. Testing only with the three examples used during development.
  4. Sending whole conversation histories or documents on every call and being surprised by the bill.

A one-week build plan

Day one: define the task, success criteria, and 30 test inputs. Day two: make the first API call with structured output and validation. Day three: run the test set and record accuracy and failures. Day four: improve instructions based on failures, one change at a time. Day five: add error handling, timeouts, logging, and cost tracking.

Days six and seven: wrap it in a minimal interface or script, write a README with the evaluation results and known limits, and ask someone to try it. By the end you have a small but honest application — a much better foundation than an impressive demo that breaks on unexpected input.

Try it yourself

Build the ticket classifier above with 30 synthetic tickets. Measure accuracy, change one instruction, and measure again.

Frequently asked questions

Which language should I use to build LLM apps?

Python and TypeScript have the most SDKs and examples. Use what your team already deploys.

Do I need a framework like LangChain?

Not for a first app. Direct API calls help you understand the basics; add frameworks when they solve a real problem.

How do I control costs?

Choose the smallest model that meets quality on your test set, cap input size, cache where possible, and monitor usage.

Want a suggested next step for your situation?

Share a few details and someone from TechlyUp will get back to you. No automated sequences.

Sources and further reading

Examples are authored practice material, not measured learner outcomes. Tool behavior can change. Found an error? Contact TechlyUp with the page URL and correction.

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