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A production LLM platform is the application system around a model—not just a model endpoint. Build it in stages: define the task and its risks, separate the system’s responsibilities, version everything that can change an answer, evaluate before release, and operate the service with security controls and end-to-end observability.
1. Define the use case and its boundaries
Start with the workflow you want to improve, not a model or framework. Write down who will use the system, what inputs it will receive, what a useful answer looks like, and what happens when it is wrong. These choices determine whether a language model is appropriate and what the platform must protect.
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Set measurable requirements
- Task quality: Define what counts as correct, relevant, grounded, or appropriately refused for this particular workflow.
- Failure impact: Identify errors that are merely inconvenient and those that could expose data, mislead a user, or trigger an unsafe action.
- Data: Inventory sensitive-data classes, source systems, access rules, and any residency requirements.
- Service targets: Estimate expected traffic and set latency, availability, and budget constraints based on the application’s needs.
- Alternatives: Check whether deterministic software, search, or an existing foundation model can meet the requirement without extra orchestration or fine-tuning.
Google Cloud’s lifecycle guidance recommends selecting a model based on its strengths, weaknesses, and costs for the use case. Compare candidates on representative tasks rather than relying on a general ranking. The same lifecycle framing treats production as an ongoing cycle of discovery, development, deployment, monitoring, and improvement.
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A prototype often puts ingestion, retrieval, prompts, model calls, user interaction, and logging in one service. That can be a reasonable way to learn, but it makes parts harder to test, update, and scale independently. AWS Prescriptive Guidance recommends discrete, loosely coupled steps for production generative AI applications.
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Logical components to plan for
- Ingestion and processing: Connect to approved sources, normalize and clean content, and prepare updates. Chunking and embedding are relevant when the application needs semantic retrieval.
- Retrieval: Find relevant, permission-appropriate material when answers must use external or enterprise data. Include retrieval in evaluation rather than assuming that a fluent answer is a grounded one.
- Model access: Put provider authentication and model selection behind a controlled interface or AI gateway where it helps centralize policy, routing, and telemetry.
- Orchestration: Sequence prompts, model calls, retrieval, tools, and deterministic business logic. Keep decisions that require predictable behavior outside the model where practical.
- Application and state: Provide the user-facing interface or API and manage session state only as the use case requires.
- Shared controls: Make identity, policy enforcement, evaluation, and observability available across the request path.
These are responsibilities, not a mandate to create one microservice per component. Start with the simplest design that preserves clear boundaries. Split services when independent scaling, ownership, security isolation, or fault containment justify the extra operational work.
3. Choose models and architecture by testing trade-offs
Run viable options against the same evaluation set and workload assumptions. Compare task quality, total operating cost, latency, capacity and reliability, privacy controls, data residency, deployment constraints, and integration effort. Reassess when model versions, provider terms, or application requirements change.
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| Decision | Evaluate | Operational consequence |
|---|---|---|
| Hosted model API or self-hosted/open model | Task quality, privacy and control, residency, capacity, latency, cost, and integration needs | A hosted API and a self-managed deployment have different control and operating burdens; decide from measured workload requirements rather than assuming a universal winner. |
| Single model call or retrieval/multi-step workflow | Whether the task needs external grounding, tools, or multiple reasoning steps | Additional stages can add capability, but also create more latency, failure paths, evaluation work, and tracing requirements. |
| Monolith or modular services | Current scale, team ownership, deployment independence, and failure boundaries | A monolith is simpler to operate initially; modularity can make testing and independent change easier as responsibilities diverge. |
| Prompting or fine-tuning | Whether evaluation shows that task-specific adaptation is needed | Choose based on evidence from the application’s test set and the operational cost of maintaining the chosen approach. |
| Provider abstraction or direct integration | Expected provider changes, routing needs, and amount of provider-specific functionality | A narrow abstraction can simplify configuration changes and comparative testing, but does not erase differences between provider APIs or model behavior. |
If retrieval is in scope, measure retrieval quality separately as well as end to end. For agents or other multi-call workflows, evaluate the entire chain—including slow, failed, and incomplete paths—because per-call performance alone does not represent the user experience.
4. Version every input that can change behavior
Reproducing an output change requires more than knowing which model was called. Track revisions for application code, prompts, model identifiers and configuration, tools, workflow definitions, retrieval data and indexes, fine-tuned adapters, and evaluation data. Attach the relevant revisions to deployments and traces.
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Google Cloud’s guidance describes generative AI lineage as extending across a chain’s data, models, code, evaluation data, and metrics. AWS recommends connecting deployments, evaluation runs, and traces to a specific code revision. Apply the same release discipline to a prompt edit or index refresh as to an application code change: each can change what users receive.
5. Build evaluation gates before launch
Create a versioned evaluation set from realistic tasks, edge cases, known failure modes, and high-risk inputs. Stabilize the evaluation approach, metrics, and ground truth early enough to compare changes meaningfully. Google Cloud’s Architecture Center emphasizes that comparability depends on stabilizing those elements as early as possible in development.
Evaluate the system at several levels
- Code-level tests: Use deterministic unit and integration tests for ordinary application logic, permissions, error handling, and tool interfaces.
- End-to-end tests: Exercise the complete workflow, including retrieval, model response, and any tools or business rules.
- Task criteria: Score the dimensions that matter to the use case, such as correctness, groundedness, relevance, instruction following, refusal behavior, latency, and cost.
- Security cases: Test prompt injection, sensitive-data exposure, and attempts to extract system instructions.
- Human review: Use human review to check evaluation rubrics and periodically audit model-assisted graders. A grader is not a substitute for a clear criterion or representative test data.
Automate evaluations in CI/CD where possible and set thresholds that block unacceptable quality regressions. AWS guidance also recommends security scans before staging. OpenAI’s Evals API is one provider-specific option for defining evaluations, runs, data sources, and graders; it is not a requirement for a provider-neutral platform.
Use staging and a defined release gate
- Deploy the candidate to a production-like staging environment and run the agreed acceptance, evaluation, and security checks.
- Record the candidate’s code, prompt, model, configuration, and data/index revisions so the tested build is identifiable.
- Set the release and rollback conditions before exposing the change to users. Use a canary or A/B test when appropriate to limit exposure and compare behavior.
- Make a formal go/no-go decision against the predefined exit criteria. AWS Prescriptive Guidance identifies this decision as the culmination of preproduction, rather than a judgment based on schedule pressure or intuition.
6. Secure model, tool, and data access
Apply security controls to each boundary: the user, application, model provider, retrieval source, and any tool the model can invoke. Store credentials securely, integrate with the organization’s identity system, and grant only the access required for the task. An agent should not inherit broad data or action permissions simply because its workflow is convenient.
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- Restrict which identities and application components can call models, access data sources, and invoke tools.
- Enforce policy and guardrails at the boundary where access or action occurs; do not rely on a prompt alone to enforce authorization.
- Log enough context for audit and incident response while applying data minimization and protecting user information.
- Review provider endpoints, retention behavior, application state, and residency requirements before sending sensitive content.
- Include adversarial security cases in evaluation, particularly prompt injection and personal or otherwise sensitive information exposure.
Provider-specific controls must be checked against the endpoint actually used. OpenAI’s API data-controls documentation says API abuse-monitoring logs may include prompts and responses and are retained for up to 30 days by default, subject to exceptions. Zero Data Retention and Modified Abuse Monitoring require approval and have endpoint-specific limitations. These statements describe OpenAI’s API controls, not other providers, and do not establish that every endpoint or form of application state is covered by the same setting.
7. Instrument the full request path
Combine application and infrastructure signals with model-specific telemetry. Correlate traces across model calls, tools, retrieval stores, and databases so an operator can locate where a request slowed down, failed, or produced a poor result.
Capture useful, policy-compliant signals
- Safe request and correlation identifiers, plus the code, prompt, model, and configuration revisions.
- Retrieval results or permitted references, tool events, and timing for each stage.
- Latency, errors, usage or token counts, and cost signals for the request.
- Evaluation signals, user feedback, and relevant quality outcomes.
- Input shifts and changes in topics or intents, using privacy-appropriate measures.
AWS recommends unified telemetry and end-to-end traces, with dashboards for latency, error rate, cost per request, token usage, quality scores, and feedback. Monitor application-level outcomes first; use component traces to diagnose them. Google Cloud describes drift measures that can include text length, token counts, vocabulary and intent changes, and embedding distances. Continuous evaluation can compare production outputs with ground truth or user ratings where those are available.
8. Define operating limits and an improvement loop
Set service objectives and alert thresholds for availability, latency, failure rates, quality, and spend. Assign incident ownership and define how the system behaves when a provider, retrieval source, or tool is unavailable.
Plan for predictable failure modes
- Use timeouts and bounded retries so a failed dependency does not create an unbounded request or cost.
- Set rate limits and capacity plans for expected demand and provider constraints.
- Choose graceful fallbacks appropriate to the task, such as a clear unavailable response or a deterministic path; do not silently present an ungrounded answer as reliable.
- Define alerting and escalation for quality regressions as well as infrastructure failures.
Use production feedback and evaluation results to decide whether to change a prompt, retrieval configuration, tool, model, or application logic. Route those changes through the same evaluation and security gates as the original release. This closes the operating loop without treating live traffic as an uncontrolled experiment.
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