AI Solutions
LLM Integration for Business Applications: Architecture, Guardrails, Cost and Evaluation
Move an LLM feature from demo to production with structured outputs, tool boundaries, authorization, prompt-injection controls, cost limits, evaluation and fallbacks.
Calling a large language model API is easy. Building an application feature that remains useful, affordable and appropriately controlled after real users arrive is the harder part. A production LLM integration needs decisions around data scope, output structure, tools, authorization, evaluation, cost, logging and failure behavior. This guide focuses on the architecture around the model rather than any single provider. Define the user job before choosing a model Start by describing the task: answer from an approved knowledge base, summarize a record, classify a request, extract fields into a schema, draft a response for a reviewer, help a user navigate an application, or propose an action that another system may execute after validation. For each task, define what a good result looks like, which data it may use, what failure looks like and whether a human must approve the result. Use structured outputs for application logic If downstream code expects category, priority, extracted fields or another schema, request structured output and validate it before use. Treat model output as untrusted application input rather than assuming that well-formed text is correct. Keep authorization outside the prompt The model should not decide which customer records, documents or privileged tools a user may access. Enforce tenant boundaries, roles and ownership in the trusted application/data layer before data enters model context or an action is executed. Design tool boundaries deliberately Tool-calling assistants should receive only the operations needed for the user task. Validate tool arguments, separate read actions from high-impact writes, require confirmation where appropriate and never expose unrestricted infrastructure credentials to model-generated calls. Plan for prompt injection User text and retrieved documents can contain instructions intended to override the application’s intent. Separate application instructions from untrusted content, keep permissions narrow and test adversarial inputs. No prompt is a substitute for access control. Choose model capability per step Not every step needs the largest model. Classification, extraction, summarization and complex reasoning may have different requirements. Measure quality, latency and cost against representative cases instead of standardizing on one model for every feature. Build evaluation before scale Create a repeatable set of real tasks with expected outputs, acceptable variations, failure cases and prohibited behaviors. Re-run it after prompt, model, retrieval or tool changes. For retrieval-backed features, evaluate retrieval and generation separately. Control cost and latency limit unnecessary context and conversation history; use structured retrieval instead of sending entire documents; use smaller models where evaluation proves they are sufficient; set per-user/tenant limits; track token/request cost by feature; cache safe deterministic work; handle retries without accidentally multiplying expensive calls. Design graceful failure Providers time out, outputs fail validation and models sometimes lack enough evidence. Define a fallback: retry with bounded logic, ask for clarification, return a deterministic interface, route to a human, or state that the system cannot safely complete the action. Log only what you need Prompts and outputs may contain private or sensitive business data. Decide what is necessary for debugging/evaluation, protect access to logs and set retention deliberately. Avoid collecting complete user conversations simply because it is technically easy. A...