Put generative AI inside a process that finishes.
Writing a paragraph is one step. The business result may be a publishable product record, an accurately prepared job file or a staff answer tied to the current procedure. Design for that result, including the checks around generated content.
Start here.
Generative AI creates or transforms content from supplied instructions and context. For business work, it is useful for drafting, extracting, classifying and explaining. It needs relevant source material, task-specific validation and an authorised destination before its output becomes an accepted business record.
A defined content job
Choose the exact output: product copy with supported specifications, extracted fields from a document, or guidance grounded in a maintained procedure.
Evidence alongside output
Retain the source, its date and any unresolved gaps. A fluent sentence is not evidence that a product feature or customer entitlement exists.
A controlled next step
Decide whether eligible output can publish automatically, needs review, or remains a draft. Keep approval separate from the act of generating words.
From an idea to a checked result
Select approved inputs
Gather the relevant documents or records and confirm the system may use them for this task. Avoid giving it unrelated customer or staff information.
Retrieve the right context
Select current, relevant passages or exact fields. Retrieval can help ground a response, but an outdated or wrong source still produces a poor basis.
Generate or extract
Request a defined output structure. Separate directly extracted facts from drafted explanation and from information the source does not provide.
Validate the result
Check required fields, exact identities, units, calculations and support for factual claims. Route ambiguous cases to review.
Publish or hand over
Write approved content to its destination, retain its source references and record what changed. Confirm the write rather than assuming the draft was published.
What changes when the work gets complicated?
A supplier record has almost no useful description
- What arrives
- A new catalogue item has a title and model number but thin customer-facing copy.
- What is checked
- Match the exact model and permitted source material; distinguish manufacturer facts from marketing wording.
- What happens next
- Prepare a description using supported attributes; hold missing or contradictory specifications for review.
- What finishes the work
- A usable product draft with source references and visible gaps, ready for the agreed publication checks.
A job pack arrives as mixed documents
- What arrives
- Emails, a PDF and photographs contain the details needed for a completion record.
- What is checked
- Check job identity, required evidence, missing pages and conflicting dates or amounts.
- What happens next
- Extract fields and assemble a draft pack; calculate totals through fixed rules and flag unresolved evidence.
- What finishes the work
- A structured record for review, rather than another summary someone must retype.
A staff member needs the current instruction
- What arrives
- A frontline worker asks how to handle an unfamiliar issue.
- What is checked
- Retrieve the applicable site procedure, effective date and staff authority.
- What happens next
- Give concise steps with the relevant source; ask for clarification or hand over when the instructions do not cover the situation.
- What finishes the work
- Guidance linked to the current procedure and a clear route for exceptions.
Match the technique to the work
Content generation, extraction and knowledge answers have different acceptance tests. Treating them as the same task hides errors.
| Output | Acceptance question | Common failure |
|---|---|---|
| Product copy | Does each factual claim match this exact model? | Blending specifications from a similar model |
| Structured document fields | Do values match the source and required format? | Dropping a page, unit or negative amount |
| Staff guidance | Is this the applicable current procedure? | Answering confidently from superseded instructions |
Drafting needs factual boundaries
Tone can vary while specifications cannot. For a product description, style may be generated, but dimensions, compatibility and included items must come from a trusted record.
Extraction needs field-level checks
A plausible total is not enough. Check document identity, line items, units and required fields. Use deterministic calculations where arithmetic controls the result.
Keep knowledge current and access appropriate
Retrieval-augmented generation means finding relevant material and supplying it as context for the answer. It helps connect the model to business information; it does not guarantee correctness.
Give knowledge an owner
Someone must decide which document supersedes another and when an answer should change. Record effective dates and remove retired instructions from the active set.
Respect record-level access
A staff question should retrieve only material that user or workflow is allowed to read. A search index is not a reason to pool every department’s documents.
Make missing information useful
An explicit unknown can become a targeted request for a model number, approval or source document. Invented detail is harder to detect and creates more rework.
Measure accepted outputs, not text volume
The useful comparison is the whole task before and after, including the cost of checking and correcting the generated result.
Track acceptance and correction
Record how many outputs meet the agreed standard, what reviewers change and how long review takes. A faster draft is not a saving if checking it takes longer.
Keep representative examples
Test short and long documents, unusual formats, contradictory sources and genuinely missing answers. Recheck this set when the model, instructions or source structure changes.
Connect the final destination
G3’s founder-owned staff system connects knowledge to guidance and recorded issues. For your process, specify whether the accepted output belongs in a shop, job system, knowledge base or work queue.
Questions worth asking
Is generative AI the same as an AI agent?
No. Generative AI produces or transforms content. An agent can use a model to choose actions and tools. You can use generative AI inside a fixed process without an open-ended agent.
Can generated descriptions publish automatically?
For a defined class of records, yes, if required facts, quality and permissions pass the agreed checks. Ambiguous or unsupported claims should remain held rather than silently becoming public copy.
Does connecting our documents stop hallucinations?
No. Relevant context can help, but retrieval can select the wrong record and the model can still misread it. Validate the fields or claims that matter to the business result.
Do we need to train our own model?
Not necessarily. Start by testing whether a suitable existing model, well-selected context and defined output checks meet the task. Custom training is a separate decision with its own data and maintenance needs.
Where do our documents go, and can a provider retain or train on them?
That depends on the selected services, deployment, account settings and applicable provider terms. Before using business data, map which content leaves your systems, where it is processed or logged, who can access it, and the retention, deletion and training-use rules. Send only the material needed for the task and agree any required restrictions. A private knowledge interface or a self-hosted workflow engine does not by itself keep every connected service local or establish a universal no-training guarantee.
Sources and further reading
Official documentation for the platform facts discussed in this guide. Business scenarios and recommendations are Canvo’s analysis.
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