AI implementation patternsPlain language

The useful shapes AI takes inside real workflows.

Use this gallery to translate vague AI ideas into concrete implementation patterns: what each pattern does, what data it needs, where it fails, and how to evaluate it.

Browse patterns Try the mapper

Start with the work

A use case is usually a bundle of patterns.

The mapper turns these examples into a deterministic worksheet: pick a workflow and see the patterns, data requirements, review points, failure modes, and evaluation questions.

Open the use case mapper

Customer support triage

Classification + retrieval + routing

Invoice processing

Document extraction + exception review

Vendor demo review

Summarization + evidence questions + evaluation

Claims intake

Extraction + routing + human approval

Sales forecasting notes

Summarization + structured fields + review

Policy help desk

Retrieval + citations + escalation

Pattern gallery

Learn the pattern before judging the tool.

Each pattern is educational, not a recommendation. The right question is not "Should we use AI?" It is "Which pattern is this, and what would need to be true?"

RetrievalPattern

Answer from a controlled body of documents instead of memory.

Data it needs: Policies, contracts, product docs, support history, knowledge bases.
Where it breaks: Weak source coverage, stale documents, poor permissions, missing citations.
Evaluation question: Can a reviewer trace every useful answer back to the right source?

RAG and groundingContext windowsEvaluation sets

Document extractionPattern

Turn messy files into structured fields a workflow can use.

Data it needs: Invoices, forms, claims, PDFs, emails, scanned packets.
Where it breaks: Layout drift, low-quality scans, ambiguous fields, no exception queue.
Evaluation question: Which fields must be right every time, and which can route to review?

Structured outputsHuman reviewConfidence thresholds

ClassificationPattern

Sort work into categories so the next step is obvious.

Data it needs: Tickets, leads, requests, claims, incidents, emails.
Where it breaks: Overlapping labels, changing policies, no ground-truth examples.
Evaluation question: Does it reduce routing time without hiding edge cases?

Taxonomy designModel evaluationEscalation paths

SummarizationPattern

Compress long context into a reviewable brief.

Data it needs: Calls, meetings, case notes, research packets, long threads.
Where it breaks: Lost nuance, unsupported specifics, summaries that sound more certain than the source.
Evaluation question: Can readers see what changed, what matters, and what remains uncertain?

CitationsAbstraction levelFactuality checks

RoutingPattern

Recommend the next queue, owner, or workflow branch.

Data it needs: Workflow history, business rules, roles, service-level targets.
Where it breaks: Unclear authority, brittle rules, no audit trail for handoffs.
Evaluation question: Does it improve first-touch routing while keeping humans in control?

Workflow orchestrationAudit logsFallbacks

CopilotPattern

Help a person draft, compare, inspect, or search inside their existing work.

Data it needs: Current task context, internal docs, templates, prior examples.
Where it breaks: Unreviewed output, weak grounding, unclear ownership of final work.
Evaluation question: Does it shorten expert work without replacing expert judgment?

Tool usePrompt contextReview loops

AgentPattern

Coordinate multiple steps across tools under explicit constraints.

Data it needs: Tool permissions, task state, policies, success criteria, logs.
Where it breaks: Too much autonomy, hidden side effects, poor stop conditions.
Evaluation question: Can it show what it did, why it stopped, and what needs approval?

Tool callingState machinesPermission boundaries

EvaluationPattern

Measure whether an AI behavior is good enough to ship or trust.

Data it needs: Gold examples, failure cases, rubrics, production feedback.
Where it breaks: Vague quality bars, test sets that do not match real work.
Evaluation question: Does the test catch the failure leaders actually care about?

Eval harnessesRegression testsSampling

PersonalizationPattern

Adapt explanation, examples, or order based on a reader or role profile.

Data it needs: Explicit preferences, role, progress, prior interactions.
Where it breaks: Creepy inference, stale memory, overfitting to a thin profile.
Evaluation question: Does it make the product clearer without pretending to know too much?

MemoryConsentProfile scope

Pro direction

Next step: turn patterns into worksheets.

The public mapper now covers common workflows with curated, deterministic guidance. A future Pro version can save a reader's worksheet, accept workflow-specific intake, and turn the map into a pilot-scoping artifact with explicit guardrails.

Try the mapper More resources