Enterprise‑Level FDE Knowledge Framework: From Business Insight to AI Engineering Delivery

1. AI Deployment and Enterprise FDE Role Understanding

Enterprise AI maturity and deployment economics

Gap, typical pitfalls, and mitigation strategies from PoC to scale‑up

Business models: deployment companies, platform delivery, internal capability centers

FDE role definition and responsibilities

Differences between FDE and traditional roles (algorithm engineer, pre‑sales consultant, product manager, project manager, architect)

FDE delivery loop: Define → Model → Design → Build → Govern → Consolidate

Industry insights from Palantir AIP / Ontology model and Anthropic / OpenAI deployment strategies

2. Business Insight and Scenario Diagnosis

Business process and domain understanding

BPMN / swim‑lane diagram creation and analysis

User journey maps and service blueprints

Business pain‑point identification and "pain chain" analysis

High‑value scenario identification and prioritization

AI intervention points: automation, augmentation, collaboration nodes

Value‑feasibility matrix (efficiency, quality, experience, risk)

Scenario‑mode decision tree for Copilot / Agent / RAG / RPA+AI

Problem reframing and goal definition

Transform vague demands into AI‑executable problem statements

Design of north‑star, process, and guardrail metrics

ROI estimation framework

3. Large Model and AI Fundamentals

Large model principles and limits

Transformer architecture, token generation, hallucination sources

Difference between inference and memory, context‑window constraints, long‑text handling strategies

Capability profiles of mainstream models (GPT‑4o, Claude, Llama, Qwen, etc.)

Model selection and cost awareness (token pricing, caching, batch processing)

Prompt and context engineering

Systematic prompt engineering: role, instruction, examples, output format, chain‑of‑thought

Dynamic context construction and window budget allocation

Multi‑turn dialogue state management and memory strategies

Multimodal basics

Understanding and generation capabilities for images, audio, video

Data fusion methods in multimodal agents

4. Knowledge Engineering and Ontology Modeling

Core ontology concepts

Entities, Relations, Properties, Rules, Actions

Ontology as the hub for enterprise digital twins and AI decision context

Entity‑relation modeling

Class diagram / property graph notation, defining 1:1, 1:N, M:N relationships

Business object identification and standardization

Rule and action modeling

Structured expression of business rules (logic expressions, decision tables)

Atomic definition of executable actions (API, function, parameters, side effects)

Permission modeling

RBAC / ABAC basics, role‑permission matrix design

Principle of least privilege for data scope and operation boundaries

Knowledge assetization and retrieval

Structured handling of documents, FAQs, tickets, specifications with metadata tagging

Text chunking, vectorization, similarity search

Hybrid BM25 + vector retrieval

Graph RAG: knowledge graph construction, community summarization, multi‑hop reasoning

Knowledge‑base versioning, update mechanisms, quality assessment

5. AI Workflow and Agent Engineering

Agent architecture patterns

Reason‑Act loop (ReAct), Plan‑Execute, multi‑agent collaboration

Multi‑agent debate, review, pipeline patterns

Workflow orchestration

Graph‑based workflow engines (LangGraph, Dify, Coze, etc.)

Nodes, conditional edges, state management, sub‑graph encapsulation

Visual workflow design and debugging

Tool invocation and standardization

Tool description standards (OpenAPI / Swagger schema)

MCP (Model Context Protocol): server/client architecture, hot‑plug tool support

A2A (Agent‑to‑Agent) protocol and cross‑agent communication

Tool registration, discovery, security management

Human‑AI collaboration design

Five collaboration modes: auto‑execute, suggest‑execute, manual confirm, pause‑to‑todo, escalation handling

Collaboration state‑machine design and experience polishing

Seamless human takeover design

Exception handling and fallback

Timeout retries, tool error rollback, hallucination detection

Safe fallback responses and alert escalation mechanisms

Irreversible operation protection

6. Engineering and Production Delivery

System integration

API integration patterns: RESTful, Webhook, message queues (Kafka), event‑driven

Business system connectivity (CRM, ERP, ticketing, R&D platforms)

Data processing pipelines

ETL / ELT basics, source connection, cleaning, masking

Agent injection methods for real‑time and batch data

Deployment and operations basics

Containerization (Docker) and serverless deployment

CI/CD pipeline fundamentals

Environment variable and configuration management

System stability

Rate limiting, circuit breaking, degradation, caching, idempotency design

Canary release and rollback strategies

Low‑code / no‑code platform usage

Rapid prototyping with Dify, Baillian, etc.

Understanding platform boundaries and evolution path from low‑code to custom development

Logging and audit

Structured log design

Immutable AI operation trace records

Audit tracking and compliance storage

7. Quality Assurance, Security, and Governance

AI evaluation system

Layered evaluation sets: normal, boundary, adversarial, multi‑turn dialogue

Metrics: accuracy, recall, adoption rate, hallucination rate, task success rate

LLM‑as‑Judge method and automated evaluation pipelines (e.g., RAGAS)

BadCase management

BadCase grading (critical, severe, moderate, suggestion)

Full lifecycle: discover → record → classify → root‑cause → fix → regression verification

BadCase conversion into evaluation assets

Security and compliance

OWASP LLM Top‑10 risk list

Prompt injection defense, jailbreak detection, sensitive content filtering

Data masking and privacy protection (PII handling)

Compliance red‑line checklist and veto items

Relevant regulations (e.g., China’s "Interim Measures for Generative AI Services")

Observability and AgentOps

OpenTelemetry‑based end‑to‑end LLM tracing

Monitoring dashboards: token consumption, latency, success rate, adoption rate

Alerting and circuit‑breaker policy configuration

Cost analysis and optimization

Quality gate and release management

AI project architecture review checklist, security checklist

Pre‑release verification workflow and signature confirmation

Production trial run and effect evaluation

8. Organizational Collaboration and Asset Consolidation

Business co‑creation facilitation

Interview techniques (five‑question method, pain chain, voting priority)

Workshop design and facilitation practice

Prototype walkthrough and rapid feedback

User empowerment and training

Operation manual authoring

Seed user cultivation strategies

"AI onboarding day" style ceremonial promotion activities

Feedback collection and process fine‑tuning

Change management basics

Stakeholder analysis and communication planning

Resistance identification and mitigation strategies

Adaptive adjustments of processes, roles, and performance metrics

Retrospective and continuous improvement

KISS / AAR retrospective methods

Five‑step review: user story → solution design → actual effect → gap analysis → improvement actions

Retrospective report writing and organizational sharing

Engineering asset consolidation and reuse

Reusable workflow templates packaging (README, dependencies, sample data)

Standardized ontology fragments and knowledge‑base structures

Modular packaging of prompt libraries, evaluation scripts, tool connectors

Internal asset marketplace construction and reuse promotion mechanisms

Transition from project‑based delivery to platform‑based, asset‑driven compounding