How to design LLM integrations that work

The failure modes discussed in the previous article are not accidental. They are structural. Successful LLM integration is less about better prompts and more about rethinking how products, APIs, and workflows are designed.

In this article, we will explore practical approaches to addressing the most common issues and increasing the chances that an LLM integration delivers real value.

Use language and semantics that match your users

The terminology used at the API level should closely match the language your users naturally use. System prompts should include a clear glossary of domain-specific terms, and those terms should be consistent across the product.

Equally important is semantic clarity. Concepts such as time ranges often hide implicit assumptions. For example, in the prompt:

"Show me my sales from last month"

Does "last month" refer to the previous 30 days, or the previous calendar month from the 1st to the last day? Without clearly defined semantics, the user experience becomes frustrating. These assumptions are often implicitly baked into frontend layers and must be made explicit when exposed to an LLM.

Avoid fragile chains of API calls

Long sequences of dependent request and response steps are inherently brittle. Each step introduces a new failure mode and compounds issues.

Where possible, avoid frontend-style automations that simply replay existing UI workflows via APIs. These are just as fragile and add a layer of indirection on top of already complex interactions.

The main exception is simple, one-shot actions with no backend side effects, for example:

"Improve this description"

For anything more complex, purpose-built backends and bulk operations are essential.

Design for transparency, review, and recovery

Trust in LLM-driven features depends on transparency and control.

Products should be designed with:

• Audit functionality that clearly distinguishes AI-initiated actions from those performed by human users
• Rollback mechanisms and bulk-edit capabilities to undo or correct mistakes efficiently
• A plan or review mode that allows users to inspect and modify proposed actions before execution

Importantly, review steps should rely on deterministic, purpose-built frontend components, not yet another layer of LLM interpretation.

If mistakes are inevitable, recovery must be cheap. Otherwise, automation simply shifts work rather than eliminating it.

Make information-gathering explainable

For non-mutating use cases, users must be able to review how an answer was derived. This includes inspecting inputs, filters, and intermediate data used by the LLM to reach its conclusion.

Explainability turns answers from opaque assertions into verifiable results and is critical for maintaining trust in analytical or reporting scenarios.

Treat security as a first-class concern

Given the vulnerabilities introduced by LLMs, access control must be explicit and fine-grained. Ideally, users should be able to control which entities, fields, and operations an LLM can access.

This often requires authenticating the LLM as a distinct principal with scoped permissions, rather than reusing the user's own identity. Access-control systems must also be end-to-end, ensuring that transitive and computed data inherit the most restrictive permissions of its source data.

When access is denied, the system should surface this clearly to the user. An explicit "I cannot access this information" is far preferable to silent failure or hallucinated responses.

Closing thoughts

LLM integrations fail not just because the technology is immature, but because most products were never designed to support intent-driven automation, probabilistic reasoning, or transparent recovery from errors.

If you want to ensure your LLM integration project is successful, or if you have already experienced some of these failures, feel free to reach out. I am available to consult on how to address these and similar challenges.