LLM Integration: A Builder's Checklist

TL;DR

• LLM integration fails most often because of skipped fundamentals, not bad models.
• Pick the right model for the job before writing a single line of code.
• Prompt structure, context limits, and cost control all need decisions up front.
• Error handling and evals are not optional extras. Build them in from the start.
• Security is the step most builders skip. It will bite you.

LLM integration is not magic. It is a set of engineering decisions. Get them right and your app works. Skip them and you end up with a system that hallucinates, costs too much, and breaks in production. This checklist covers every major decision point. Work through it in order before you ship anything.

If you want a broader starting point, read how to build an AI app first. Then come back here.

---

1. Have you chosen the right model for the job?

This is the first decision. It affects everything else. Start with these questions:

• How complex is the task? Simple extraction or classification does not need a frontier model.
• How fast does the response need to be? Bigger models are slower.
• What is the cost per token? Run the numbers before you commit.
• Does the model need to follow long instructions reliably? Not all models do this equally well.

For most production use cases, start with a mid-tier model. Only move up if the outputs are not good enough. Defaulting to the most powerful model burns budget and adds latency without always improving results. Read the Anthropic API getting started guide if you are still evaluating which provider to build on.

Pick the cheapest model that gives you acceptable output. Move up only when you have data that justifies it.

---

2. Is your prompt structure doing the heavy lifting?

Most bad outputs come from bad prompts. Not bad models. A solid prompt has three parts:

1. A system prompt that sets the role and rules.
2. A user message that states the task clearly.
3. Output formatting instructions that tell the model exactly what to return.

Keep your system prompt tight. One role. Clear rules. No ambiguity. Avoid vague instructions like "be helpful" or "do your best." Tell the model exactly what to do and what not to do. Test your prompts against edge cases before you go live. If a prompt fails on five weird inputs, it will fail on real users too. See AI API integration patterns for more on structuring calls cleanly.

---

3. Are you managing context the right way?

Every LLM has a context window. It is finite. You need a plan for it. Key decisions:

• What goes in the context for every call? (System prompt, user history, retrieved documents?)
• What do you trim or summarise when the window fills up?
• Are you passing the full conversation history every time? That gets expensive fast.

For apps that need to remember things across sessions, you need a retrieval strategy. That is a separate topic. Read RAG vs fine-tuning to understand your options. One rule: never stuff the context window and hope for the best. Prioritise what matters. Drop what does not.

Treat context like RAM. It is limited, and what you load in has a direct effect on output quality and cost.

---

4. Have you run the cost numbers?

LLM costs sneak up on you. A feature that looks cheap in testing can become expensive at scale. Do this before you ship:

• Calculate average tokens per call (input plus output).
• Multiply by your expected call volume per day.
• Factor in your model's per-token pricing.
• Add a 30% buffer for unexpected usage spikes.

Set up cost alerts with your provider. Do not wait for a surprise bill. If costs are too high, look at these levers first: shorten your system prompt, summarise conversation history instead of passing it all, cache responses for repeated or similar queries, and downgrade the model for low-complexity tasks. At Devwiz we have seen apps cut LLM costs by 60% just by trimming context and caching common outputs.

---

5. What happens when the model returns garbage?

It will. At some point, it will return something unexpected. You need:

• Output validation. Check that the response matches the expected format before you use it.
• A retry strategy. If the output fails validation, retry with a corrective prompt.
• A fallback. If retries fail, return a safe default or show the user an error.

For structured outputs, use structured output features where your provider supports them. Do not parse free-text responses with string matching. It breaks. Log every failed output. That data tells you where your prompts need work.

Build error handling before you build features. It is cheaper to fix failures in testing than in production.

---

6. Do you have evals set up?

Evals are how you know if your LLM integration is actually working. Most builders skip this step. They test manually a few times, it looks fine, and they ship. Then something drifts in production and nobody notices. A basic eval setup:

• A set of test inputs with expected outputs.
• An automated check that runs on every prompt change.
• A way to score outputs (exact match, semantic similarity, or human review for complex cases).

You do not need a fancy framework to start. A spreadsheet with inputs, expected outputs, and a pass/fail column is better than nothing. Add evals before you change a prompt in production. Not after.

---

7. Have you thought through the security risks?

LLM integration opens up attack surfaces that traditional software does not have. The main ones to know:

Prompt injection. A user crafts input designed to override your system prompt. If your app takes user input and passes it directly to the model, you are exposed. Sanitise inputs. Separate user content from instructions in your prompt structure.

Data leakage. If your context includes sensitive data, the model might repeat it back in unexpected ways. Be deliberate about what you pass in.

Excessive permissions. If your LLM can take actions (call APIs, write to databases, send messages), scope those permissions tightly. The model should only be able to do what it needs to do for the specific task.

Over-reliance on outputs. Never pass LLM output directly to a database query, shell command, or external API call without validation. Treat model output as untrusted input.

These are not edge cases. They are known attack patterns. Build your defences in from the start.

Your LLM is only as secure as the guardrails you put around it.

---

FAQ

How do I choose between GPT-4o and Claude for my integration?
Test both on your actual use case with real examples. Look at output quality, latency, and cost per call for your specific task. Do not pick a model based on benchmarks alone.

What is the fastest way to reduce LLM costs?
Trim your context. Cut anything from the prompt that is not directly needed for the task. Cache responses for repeated queries. Then consider dropping to a cheaper model and testing if quality holds.

Do I need evals if I am building a small internal tool?
Yes. Even for internal tools, prompt changes without evals mean you are flying blind. A basic test suite takes an hour to set up and saves you hours of debugging later.

How do I stop prompt injection attacks?
Keep user input separate from system instructions in your prompt structure. Never paste raw user input directly into your system prompt. Validate inputs before they reach the model.

---

Ready to build your LLM integration properly?

If you want help getting this right from the start, book a session and we will work through your integration together.