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Grounded AI generation
Generative AI consulting
Generative AI consulting helps a business design and implement workflows that produce new content, such as drafts, summaries or structured responses. predeploy focuses on the source evidence, retrieval rules, review standard and evaluation needed to make that content usable for a defined business task.
Get a grounded generation workflow with reviewable sources, representative test cases and an explicit policy for missing evidence.
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
NCSC guidance covers design, development, deployment and operation. NCSC: secure AI system development ↗
New to the topic? Read the background on artificial intelligence.
Grounded AI generation
what should the system generate?
Define a useful output and the evidence it must rely on.
A drafting task needs more than a preferred writing style: specify the question answered, the approved source material, the required structure and the claims that should remain absent when evidence is missing.
Our AI consultancy helps scope the business task, while AI strategy consulting compares it with other potential investments before engineering begins.
An illustrative proposal assistant may draft a response from approved service descriptions and project evidence, with a bid owner checking every factual commitment before the response is submitted.
For other engagement types and how to scope them, see AI consulting UK.
Define the output’s purpose.
Identify approved evidence.
Name claims that require review.
Grounded AI generation
what does grounding require in practice?
Give the generation step relevant, permitted source material and preserve a route back to it.
A retrieval design needs rules for selecting current documents, respecting permissions and handling conflicting versions; providing documents to a model does not remove the need to check whether the resulting claims are supported.
AI integration services connects authoritative sources, and AI technical consultancy examines retrieval design and access boundaries for the actual application.
For the illustrative proposal assistant, attach references to the evidence used and flag a missing answer explicitly, so the reviewer can distinguish a supported draft from wording that still needs research.
Grounded AI generation
how should we handle invented content?
Test unsupported claims and give the workflow an explicit way to decline or ask for information.
The NIST generative AI profile identifies confabulation as a risk, including confidently presented false content and misleading citations.
AI evaluation consulting turns that concern into task-specific examples, such as a request for a certification the source documents do not establish or a quotation the business has never approved.
The check should inspect the claim and its cited evidence, because a plausible reference or confident explanation is itself something the reviewer must verify rather than accept as proof.
| Check | What to establish |
|---|---|
| 1 | Include absent-evidence tests. |
| 2 | Verify cited support. |
| 3 | Define a clear refusal or escalation. |
Grounded AI generation
how do we evaluate useful output?
Judge the complete business answer with an explicit reviewer standard.
Choose normal work and difficult cases, then define whether the output answers the question, uses permitted evidence and leaves the reviewer with a manageable correction task.
AI training prepares staff to apply that standard, while AI workflow automation places checking and correction at the right handoff before a consequential action.
For the illustrative proposal assistant, compare a complete answer, an unsupported commitment and a correctly flagged gap, preserving disagreements so the team can refine ambiguous criteria before release.
Grounded AI generation
what must be reviewed when the system changes?
Recheck behaviour when source material, retrieval, prompts or models change.
A new source collection or a different model can change the draft even if the application’s interface is unchanged; keep a record of the dependencies associated with each evaluated release.
The NCSC secure AI development guidelines cover operation and update management, providing lifecycle guidance alongside the workflow’s business-quality checks.
AI implementation consulting carries the system into production, while AI operations consulting assigns who reviews feedback and decides when a behavioural change needs fresh acceptance.
Version behaviour-changing inputs.
Repeat relevant evaluation checks.
Assign source and quality owners.
Make the decision concrete
What does the current task cost in time?
Choose a task connected to generative ai consulting and enter its current workload. This gives you a baseline to discuss before buying training or commissioning a build.
A baseline is useful even if you decide to keep the process manual. Measure the work that matters, then compare quality and review effort as well as time.
Assess the workflow before changing it, or plan business AI training if the gap is how people use existing tools.
Your current workload
Estimate how much time the existing task takes before discussing generative ai consulting.
16 hours per week
People × minutes × weekly tasks ÷ 60. A workload baseline from your inputs, not a prediction of AI time savings. Include review and support time when comparing a proposed workflow.
A useful AI investment leaves your team able to do the work and judge the result.
Continue with the right question
Where should you go next?
Use the overview for context, then follow the pages that match your delivery decision.
See our ai consultancy service or read the ai implementation consulting overview.
FAQ
Questions, answered
Evidence and further reading
Which sources support the guidance?
Source links support the specific background described here. The practical checklists and illustrative examples are predeploy’s guidance.
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
NCSC guidance covers design, development, deployment and operation. NCSC: secure AI system development ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
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