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Capability 23 | Hybrid Intelligence

Generative AI and Large Language Models

Generative AI and large language model solutions produce, retrieve, transform, or reason over content within defined evidence, privacy, and authority boundaries.

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Hybrid Intelligence
Intended audience and boundary

Where Generative AI and Large Language Models must earn a decision

Knowledge teams, product owners, engineering leaders, and governance functions considering language-enabled applications.

Capability scope

Workstreams within Generative AI and Large Language Models

  • Use-case architecture and model comparison
  • System instruction, prompt, tool, and agent workflow design
  • Retrieval, ingestion, indexing, and citation engineering
  • Quality, safety, privacy, and adversarial evaluation
Usable outputs

Deliverables that make Generative AI and Large Language Models actionable

  • Generative application, assistant, or secure API
  • Document ingestion and retrieval pipeline
  • Representative evaluation suite and results
  • Model card, operating controls, and maintenance guide
Evidence-led sequence

A working path for Generative AI and Large Language Models

Quality, permissions, and currency of grounding sources

  1. 01

    Frame the decision: Which model and hosting approach fit the task and data policy

  2. 02

    Prepare around this operating condition: Variability and unsupported content in model responses

  3. 03

    Build the capability in a bounded slice: Use-case architecture and model comparison

  4. 04

    Validate with this evidence: Grounded task correctness

  5. 05

    Complete the stage with this usable output: Generative application, assistant, or secure API

Service lifecycle infographic

Trace Generative AI and Large Language Models from question to observable evidence

01

Which model and hosting approach fit the task and data policy

02

Use-case architecture and model comparison

03

Generative application, assistant, or secure API

04

Require grounding, citations, or refusal for factual knowledge tasks

05

Grounded task correctness

Operating design

Conditions that shape Generative AI and Large Language Models

  • Variability and unsupported content in model responses
  • Quality, permissions, and currency of grounding sources
  • Data location, provider dependence, latency, and token cost
Authority and recovery

Safeguards for Generative AI and Large Language Models

  • Require grounding, citations, or refusal for factual knowledge tasks
  • Constrain tools, schemas, permissions, and consequential actions
  • Test prompt injection, data leakage, misuse, and unsafe outputs
Representative applications

Three ways to examine Generative AI and Large Language Models

The examples consider searching internal knowledge with source citations, drafting documents from approved facts and templates, and assisting code or document review with human verification; none is presented as client evidence.

01

Searching internal knowledge with source citations

Evaluation for searching internal knowledge with source citations would examine grounded task correctness while applying this control: Require grounding, citations, or refusal for factual knowledge tasks

02

Drafting documents from approved facts and templates

Evaluation for drafting documents from approved facts and templates would examine citation validity and evidence coverage while applying this control: Constrain tools, schemas, permissions, and consequential actions

03

Assisting code or document review with human verification

Evaluation for assisting code or document review with human verification would examine safety, refusal, and policy compliance while applying this control: Test prompt injection, data leakage, misuse, and unsafe outputs

Evaluation signals

Evidence for a Generative AI and Large Language Models decision

  • Grounded task correctness
  • Citation validity and evidence coverage
  • Safety, refusal, and policy compliance
  • Response latency and resource cost
Engagement choices

Match the Generative AI and Large Language Models scope to its uncertainty

  • A focused discovery and decision workshop for Generative AI and Large Language Models
  • A bounded Generative AI and Large Language Models feasibility, architecture, or proof engagement with defined gates
  • Generative AI and Large Language Models implementation, validation, handover, and operating support for an approved scope
Related capabilities

Adjacent capabilities around Generative AI and Large Language Models

Human-AI Collaboration SystemsRelated to Generative AI and Large Language Models: Which tasks should be automated, assisted, reviewed, or reserved for people.Decision Intelligence SolutionsRelated to Generative AI and Large Language Models: Which objectives and tradeoffs should govern the decision.Conversational AI and Virtual AssistantsRelated to Generative AI and Large Language Models: Which user requests and channels the assistant should support.
Frequently asked questions

Questions about Generative AI and Large Language Models

Usually not. Model selection, retrieval, prompting, or targeted adaptation may satisfy the requirement.

Use trusted retrieval, citations, constrained tools, refusal rules, evaluation sets, and human review.

Only with approved providers, access controls, encryption, retention limits, and suitable data agreements.

Retrieval suits changing factual knowledge, while fine-tuning is more suitable for stable behavior patterns.

Yes, but only through allowlisted tools, limited permissions, validation, logging, and appropriate approval.

Explore Generative AI and Large Language Models for a real operating question.

Bring this decision to the conversation: Which model and hosting approach fit the task and data policy A useful first output could be generative application, assistant, or secure api.

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