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 it

Start 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 pilot

Instrumentation 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.