A language model is not a normal software component. The same input can produce different outputs, it can be confidently wrong, and users can try to steer it somewhere you never intended. None of that makes it unusable — it means the engineering around the model matters as much as the model.
These are the guardrails we treat as the minimum for any LLM workflow that reaches real users. Most are ordinary software engineering, applied deliberately.
1. Constrain the input
- Limit input length and reject obviously malformed requests before they reach the model.
- Separate user content from system instructions clearly, and never let user text be treated as instructions.
- Screen for prompt-injection patterns when the model reads external content such as emails or web pages.
2. Constrain the output
Wherever possible, ask for structured output — a defined schema rather than free text — and validate it in code. If the response doesn't match the schema, retry once, then fall back. Never pass unvalidated model output to a database, a shell or another system.
3. Limit what the model can do
When a model can call tools or take actions, give it the narrowest permissions possible. Read-only by default. Actions that change data, spend money or contact people should require confirmation from a human or pass strict rule-based checks.
4. Ground answers in your data
For factual questions, retrieve relevant documents and instruct the model to answer only from them, with references. If the sources don't contain the answer, the correct response is to say so — and your evaluation set should test that it does.
5. Plan for failure
- Timeouts on every model call, with a sensible user-facing message.
- A fallback provider or model for critical paths.
- A non-AI path — search, a form, a human — when the model can't help.
- Rate limits per user to control cost and abuse.
6. Measure continuously
Log prompts, responses and outcomes (with sensitive data handled appropriately), run the evaluation set on every change, and sample live conversations for human review. Model providers update their models; your data changes; users find new edge cases. Quality has to be watched, not assumed.
Guardrails make ambition possible
It's tempting to see all this as friction. In practice it's the opposite: once the guardrails are in place, teams can give AI workflows more responsibility with confidence, because they'll know quickly when something goes wrong — and the damage will be contained when it does.
Written by
DevTechnology Engineering