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Start with one user problem, choose the simplest AI pattern that can solve it, and measure whether it works before expanding. A direct model call can handle many drafting or summarization tasks; use retrieval-augmented generation (RAG) when answers need proprietary or current information; add an agent only when the task genuinely needs multiple steps and tool use. Keep model access on the server, enforce existing tenant permissions throughout the data path, and test quality, latency, safety, and cost before release.
Choose the AI pattern that matches the job
The key decision is what information the feature needs and whether it must take actions. A chatbot is only one possible interface; AI may also support a workflow already in your product, such as summarizing an account record or drafting a response for a user to review.
| Pattern | Good fit | Validate before launch |
|---|---|---|
| Prompting | Summarization, content generation, and simple classification that can use general model knowledge or reasoning. | Behavior on representative examples, latency, cost, safety, and handling of errors or unusable answers. |
| RAG | Answers grounded in proprietary, product-specific, or up-to-date documents. | Permissions, ingestion and chunking, embeddings, vector storage, retrieval relevance, provenance, and access filters. |
| Agentic workflow | A task that needs several steps and model-directed use of tools, APIs, or data sources. | Tool security and reliability, bounded permissions, planning and execution, latency, and recovery when a step fails. |
| Fine-tuning | A narrow style, format, terminology, or repetitive task that prompting or RAG cannot meet adequately. | Whether the expected quality improvement justifies preparing, evaluating, and maintaining the fine-tune. |
AWS Prescriptive Guidance describes RAG as a way to provide relevant context that can help mitigate hallucinations, not as a guarantee of correctness. Retrieval can return irrelevant or unauthorized material, so evaluate the retrieval pipeline as well as the generated answer. Avoid beginning with an agent if a direct model call or deterministic application logic can meet the need.
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When a model API is enough
For a bounded feature such as drafting a support reply from text a user has already opened, a backend service calling a hosted model API may be sufficient. You still need to define permitted inputs and outputs, handle provider failures, and decide what the user can review or edit before acting on the result.
When to use RAG
Choose RAG when the model needs to answer from information your product controls, such as an organization’s documentation or a current knowledge base. RAG adds a data pipeline: prepare and validate sources, split them into usable chunks, create embeddings, store and retrieve those representations, apply permissions, and supply relevant context to the model. Good prompt wording cannot compensate for poor source data or retrieval.
When an agent is justified
An agent can coordinate a multi-step task and call tools, but each tool introduces a permission and failure boundary. Use it only where the product need requires that flexibility. Make allowed actions explicit, limit what each tool can access, and provide a way to stop, recover, or route uncertain work for user review.
When fine-tuning may be worth it
Fine-tuning is a narrower intervention than adding product knowledge. It may help with a consistent format, style, or repetitive task, but it brings data preparation and ongoing maintenance. Compare it against prompt changes and, where relevant, RAG using the same representative cases before committing to it.
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Plan the feature before choosing infrastructure
1. Define an outcome and a baseline
Choose one bounded user task, such as drafting a response, summarizing an account record, or answering questions about authorized documentation. Record how the existing workflow performs so you can judge whether the AI path improves it. Define what counts as useful, what requires clarification, when the system should refuse, and when the user should fall back to the existing workflow.
2. Map data, permissions, and retention
List the data that could enter a prompt, be retrieved, appear in a response, or be written to logs. For each source, identify who may access it, how tenant and user permissions are enforced, and how long the data may be retained. Check the provider’s current service-specific terms and the privacy and legal requirements that apply to your data and jurisdictions; architecture guidance alone cannot settle those obligations.
3. Compare hosting and model options
Hosted APIs and self-hosted or open-source models involve different balances of privacy, compliance, cost, customization, scalability, and operational work. Managed hosting can reduce infrastructure work; self-managed deployments can offer more configuration control but require a team able to run them. Neither option is automatically best or cheapest for every workload. AWS’s proof-of-concept guidance recommends assessing nonfunctional requirements and unit economics alongside the model approach.
Compare candidate approaches against the same workload and constraints:
- Task fit: Does the feature need general model capability, proprietary or current grounding, or multi-step actions?
- Privacy and compliance: Can the chosen provider and data flow meet the product’s obligations?
- Quality: Does it work on representative cases, including difficult and unanswerable ones?
- Performance: Is latency acceptable at expected concurrency?
- Cost per task: What do model usage, retrieval, storage, compute, logging, and operations add up to?
- Portability and effort: How much customization is needed, and can the team operate the design?
4. Put provider access behind your backend
Keep API credentials and model-provider calls on trusted server-side infrastructure, not in browser code or a mobile client. A backend boundary lets your application apply authorization, validate requests, manage timeouts and errors, and change or compare model configurations without exposing provider credentials to end users. A model gateway or abstraction can centralize this work where it is useful.
5. Build only the data flow the feature needs
For a feature without retrieval, avoid creating an unnecessary document platform. For RAG, implement source validation, cleaning, chunking, embeddings, storage, retrieval, and response grounding as a connected pipeline. Track which source material informed an answer when the product requires provenance, and test that each retrieved item is both relevant and authorized.
6. Add safety and security controls
Validate inputs and source material, constrain tool permissions, and check outputs before they reach users or trigger actions. Treat user content and retrieved documents as data, not trusted instructions. For sensitive workflows, decide when to withhold an answer, ask for clarification, or require a person to review an action.
7. Evaluate with realistic cases
Build a test set from the feature’s real workflow, with expected outcomes and permission context. Include cases the system should answer, cases it should not be able to answer, adversarial inputs, retrieval failures, and attempts to cross tenant boundaries. Measure answer quality, retrieval relevance where applicable, latency, failures, and cost. The cited architecture guidance recommends evaluating the system and its operational requirements; it does not establish a universal quality threshold, so define one that matches the product risk.
8. Release gradually and keep a fallback
Start with a limited release and monitor operational health and user feedback. Keep a way to disable the AI path or return users to the existing workflow if quality, latency, cost, or provider availability becomes unacceptable. Expand only when observed results support it.
Protect customer data and tenant boundaries
Using a third-party pretrained model does not transfer responsibility for the SaaS application’s data handling. AWS’s generative AI security guidance frames this as a shared-responsibility boundary: the provider controls its pretrained model and training data, while the application builder controls its application and the customer data it uses. That distinction does not replace reviewing the terms for the specific provider service in use.
For a RAG feature, enforce protections at every stage rather than relying on a prompt instruction to keep tenants separate:
- Ingestion: Validate sources and screen for malicious or injected content before indexing.
- Storage: Apply appropriate access controls and encryption to documents, embeddings, and related metadata.
- Retrieval: Apply the same user and tenant authorization rules used elsewhere in the product. Filter before retrieved content is passed to the model.
- Inference and output: Check whether sensitive information may be returned to this user, and apply safeguards before displaying or acting on a result.
- Logs and traces: Limit sensitive content, restrict access, set retention deliberately, and preserve enough provenance and auditability to investigate problems.
AWS guidance identifies risks including data exfiltration, poisoned RAG sources, insufficient authorization, sensitive disclosure in generated outputs, and weak provenance. Google Cloud’s reference architecture also describes least-privilege service access, data protection for prompts, responses and logs, audit logging, and regional controls. Those are design examples tied to a particular cloud architecture, not guarantees that every deployment inherits automatically.
Design production architecture and observability for the workload
A modest feature may need only a backend service that calls a model. As the workflow grows, separate concerns such as ingestion, model access, orchestration, retrieval, and the user-facing application where independent testing, updating, scaling, or monitoring provides a real benefit. AWS production guidance warns that a single monolithic component handling a complex AI workflow can become brittle and difficult to test. This is a scaling option, not a requirement to launch a network of services on day one.
Capture enough operational detail to diagnose quality and cost without retaining customer content unnecessarily. Depending on the feature and privacy commitments, useful measurements can include request volume, model and configuration version, latency, token or other unit consumption, errors, retrieval outcomes, tool calls, and user feedback. Control access to telemetry and set retention consistently with contractual and privacy obligations. AWS discusses gateways and observability for production systems; Google Cloud’s reference designs include logging, monitoring, offline analysis, and cost controls.
Estimate cost and performance with the real workload
There is no useful generic monthly bill for an AI feature: the total depends on model choice, request volume, prompt and response size, retrieval and storage design, compute, region, concurrency, logging, and provider pricing. Estimate cost per user action or task using realistic inputs and include the full system, not only model calls. AWS guidance calls out unit costs such as tokens, GPU hours, storage, and data egress alongside latency and concurrency. Google Cloud notes that vector-search costs vary with index size, queries per second, and node count, and discusses batching or autoscaling where suitable.
Benchmark the expected workload rather than assuming that a successful single request predicts production behavior. Larger prompts or more retrieved context can affect both response time and usage; retrieval infrastructure and operational monitoring also consume resources. Test expected concurrency, define timeouts and failure behavior, and use observed usage to revisit the estimate after a limited release.
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Keep the decision tied to the original user outcome. Expand the feature only when the evaluation cases show acceptable quality and permission handling, operational measures meet the product’s needs, and the complete per-task cost is understood. If the chosen pattern misses the target, identify whether the cause is the model, prompt, source data, retrieval, permissions, or workflow design before adding complexity.
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