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AI Reference Architecture: a One-Page Overview that Actually Ships

Engineering “intelligence” into products is less about a single model and more about a dependable system around it. This is a reference architecture that aligns data, models, orchestration, evaluation, and governance so teams can move from pilots to production without losing control over quality, cost, or risk.At a glance, the stack is layered as:Data sources → DataOps and governance → gold datasets → retrieval and accelerators → tool use and orchestration → ModelOps and evaluation → observability and cost control → secure product integrations.Data sources and contracts. Start by inventorying first-party systems of record, event streams, files, partner feeds, and knowledge bases. Every source needs a schema, freshness target, lineage, and a privacy profile. The NIST AI Risk Management Framework’s trustworthiness characteristics are a useful lens here: valid and reliable, secure and resilient, explainable, privacy enhanced, and fair. Defining these properties early prevents “unknown unknowns” later in model behavior.DataOps and governance. Ingest through declarative pipelines with quality checks and lineage capture. Promote into bronze, silver, and gold layers with contract tests on each hop. The goal is to make bad data hard to enter and easy to trace. When this discipline is in place, downstream retrieval, evaluation, and rollback become mechanical rather than heroic. NIST’s RMF emphasizes risk controls across the lifecycle, which maps cleanly to these gates.Golden Data Platform. Create governed, versioned datasets for the assistant to read from. This is your non-parametric memory. It should be queryable, time-travel capable, and auditable, with role-based access. Treat the gold layer as the contract between data producers and AI consumers. Retrieval depends on this layer being both accurate and attributable. The original Retrieval-Augmented Generation work formalized the idea of mixing parametric and non-parametric memory to improve factuality while providing provenance.Retrieval and accelerators. Retrieval sits on top of gold data. Use embeddings with chunking, metadata filters, and reranking to assemble context that is specific, recent, and attributable. Add domain accelerators where it helps: decision intelligence, fraud and risk scoring, campaign optimization, or behavior modeling. The technical objective is consistent grounding so the assistant answers with facts and citations rather than guesses. RAG’s benefits on knowledge-intensive tasks are well documented and remain a strong default for enterprise assistants.Tool use and orchestration. Many business tasks are procedural. Expose verified tools for lookups, pricing rules, eligibility checks, ticket creation, or order actions. Orchestrate multi-step tasks with retries, timeouts, and fallbacks. Keep a policy layer between the assistant and tools so inputs and outputs are validated. This is where “agentic” patterns are valuable, but only when bounded by clear rules tied to system-level SLOs. The RMF’s emphasis on accountability and transparency should guide how tools are approved and audited.ModelOps and evaluation. Treat models like software, but add dataset and metric governance. Register every model with lineage, versions, stage transitions, and annotations. Attach evaluation suites for accuracy, toxicity, drift, cost, and latency. Gate releases on thresholds and enable instant rollback to a known good version. A model registry such as MLflow’s provides primitives for lineage, versioning, aliases, and stage transitions that make this practical at scale.Observability and cost control. Capture prompts, retrieved context, tool inputs and outputs, and user outcomes as traces. Emit metrics and logs that flow to a vendor-neutral standard so you are not locked into one APM. OpenTelemetry is the cross-vendor, CNCF-backed standard that unifies metrics, logs, and traces, and it is the right default for AI pipelines as well as the surrounding services. This enables real SLOs: P95 latency, success rate, rollback events, cache hit rate, and cost per successful task.Security, privacy, and policy. Assume adversarial prompts, data leakage risks, and tool abuse. Enforce input and output filters, PII masking, and allow-lists for tools. Keep red-team suites and jailbreak tests in your evaluation harness. Map controls to a recognized framework so audits are repeatable. NIST’s RMF offers a concrete vocabulary to document risks, controls, and residual exposure as the system evolves.Integration with products. Deliver through stable APIs and service contracts. Hide model churn behind versioned endpoints. Provide product teams with clear SLAs and a dependency bill of materials so they can plan releases without chasing the model of the week. Document “known failure modes” and user-visible fallbacks so the experience remains reliable when upstream systems are down.What “good” looks like. Day one, you can explain where any answer came from, with a link to the retrieved evidence and the model and tool versions used. Day two, you can reproduce that answer from stored traces. Day three, you can ship an improvement behind a flag and roll it back in minutes if evaluation fails. Day four, you can quantify cost drivers and quality shifts. That loop only works when the whole architecture is in place, not just the model.Who owns what. Data engineering owns sources, contracts, quality, and gold datasets. Platform owns pipelines, storage, identity, and secrets. ModelOps owns registry, evaluation, and release control. App engineering owns orchestration, tools, and product integration. Security and compliance set policy and verify controls. Product defines the acceptance tests that matter to users. Shared ownership with crisp boundaries is what keeps AI shipping.Why it matters. Without this architecture, teams ship demo-grade assistants that are expensive to run, hard to audit, and slow to fix. With it, you get reproducibility, faster iteration, and a clear path to scale. That is why most ModelOps definitions center on lifecycle governance across many model types, not just machine learning, and why a standard registry plus open observability are non-negotiable in enterprise settings.

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Ship a Grounded Assistant in Two Weeks

You do not need a giant team or a blank check to ship a reliable, useful assistant. You need a focused plan, clear gates, and the discipline to treat AI as a system, not a demo. This blog lays out a two week path to take an assistant from idea to a controlled pilot, grounded on your data and wired into your stack with evaluation, observability, and guardrails. Week 1: get the truth in, then build on top of itStart by deciding what counts as truth. List your systems of record, partner feeds, files, and knowledge bases. For each, write down schema, refresh cadence, and ownership. Land raw data, promote it into a clean gold layer with quality checks, and tag sensitive fields. Your assistant will ground on this gold layer, not on stale dashboards or tribal knowledge.Now add retrieval over that gold layer. Use embeddings, chunking, metadata filters, and reranking so that the context you pass to the model is specific, recent, and attributable. This is the heart of retrieval augmented generation, which combines the model’s internal knowledge with an external store to improve factuality and give you provenance. When you cite sources in answers, trust grows and hallucinations fall.Give the assistant a first mile experience that feels like your product. Implement prompt templates for top tasks and keep them in version control. Wire read only tools for lookups and status checks where the answer requires an API call rather than a paragraph. Put a simple policy layer between the assistant and any tool. Validate inputs and outputs. Time out slow calls and provide fallbacks. This is where many teams are tempted to jump to “agents.” Resist that urge until the basics are in place.Set up a model registry and treat models like software. Every model and prompt set needs a version, a stage, and a promotion rule. Use a registry that supports staging and production, stage transitions with approval, and easy rollback. MLflow’s Model Registry provides these primitives and also supports aliases so you can promote a new champion without changing application code.Close Week 1 by adding evaluation as code. Write a small but honest test set that reflects how people will actually use the assistant. Measure task accuracy for your top intents, retrieval quality for knowledge tasks, and basic safety checks. Store every evaluation run with the model version and the dataset hash. Releases should fail fast if metrics dip below thresholds. This turns quality into a gate, not an afterthought.Week 2: make it observable, safe, and ready for a pilotInstrumentation is not optional. Capture traces that include the user prompt, retrieved snippets and their IDs, tool inputs and outputs, and the model response. Emit metrics for success rate, latency, and cost per task. Export logs, metrics, and traces through OpenTelemetry so you can use the APM of your choice and avoid lock in. With traces and metrics in place you can define real SLOs, burn alerts, and runbooks.Harden the surface. Red team for prompt injection and insecure output handling. Strip or neutralize untrusted instructions that arrive inside retrieved content. Keep secrets and system prompts out of responses. Allow list the tools the assistant can call and require explicit confirmation for anything that changes state. The OWASP Top 10 for LLM applications documents these exact risks and gives you practical mitigations to adopt before launch.Add a basic governance spine. Map your risks and controls to a known framework so you can explain decisions to security, legal, and audit. The NIST AI Risk Management Framework is a good default and its Generative AI profile offers concrete guidance for this class of systems. Use its language to describe how you manage validity, security, privacy, and explainability across the lifecycle.Define what good looks like for the pilot. Pick three or four metrics that matter to users and leaders. For most assistants, that set includes task success rate, time to complete a task, P95 latency, escalation rate to a human, and cost per successful task. For knowledge tasks, add retrieval precision and recall on your test set. For every metric, set a threshold that blocks release and a target you aim to beat as learning compounds. Publish these numbers so the whole team knows the goal.Run a limited pilot. Choose a cohort and a contained surface, such as a single internal team or a slice of customer traffic. Feature flag the assistant and watch your traces. When a response is wrong, follow the evidence. Was the retrieved document wrong or stale. Did the reranker choose a poor snippet. Did the tool return an edge case. Fix the specific failure in the layer that owns it rather than trying to out prompt the model. That habit is what makes the system maintainable.Keep your loop tight. Each workday, review a small set of failures and a small set of successes. Add or fix retrieval documents, tweak chunking or metadata, refine a prompt template, or update a tool contract. Re run the evaluation suite and promote a new version through the registry when it clears the gates. You will feel slow for a few days, then the practices compound. The assistant becomes more accurate, more predictable, and cheaper to run because your cache hit rate rises and your prompts stay small.Here is a simple schedule that often works.Days one and two focus on data contracts and a gold layer. Days three and four build retrieval with citations over that layer. Days five and six shape the first mile and safe tool calls. Days seven and eight wire the registry and evaluation gates. Days nine and ten add observability and SLOs. Days eleven and twelve harden security with red teaming and policy as code. Days thirteen and fourteen run a limited release with a go or no go check against thresholds. None of this requires a massive platform. It requires choosing boring, proven parts and using them well.By the end of two weeks you should be able to do three things on demand. You can explain any answer with linked evidence and the exact versions of model, prompts, and tools used. You can reproduce the answer from stored traces and logs. You can ship a fix behind a flag and roll it back in minutes if evaluation fails. That is what production ready means in practice. It is not about a single clever prompt. It is about a system that earns trust through provenance, governance, and control.If you want a one page checklist to track this plan, start with registry stages and aliases for safe promotion and rollback, OpenTelemetry for traces and SLOs, retrieval with citations over governed data, and the OWASP LLM controls for the common failure modes. Grounding, governance, and guardrails travel well across teams and use cases. The shine of a model fades. The system endures.

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