Lessons From The Field: The How of GenAI
Mohamed Shaaban�Global Director of AI Product, SCALE AI
Research & Data
Applications
Pre-Training*
Deployment*
User reported issues
General Post-Training (SFT + RLHF)
Red Teaming
Specialized Post-Training (e.g RLVR)
Model Evaluation
*Pre-Training, Post-training and Deployment are managed by customers.
APPS + AGENTS
DIALECT
DATA + MODELS
SGP AI Infra
AGENT OUTPUTS
SGP Apps and Workflows
DECISIONS
Dialect
EVALS
ENTERPRISE OVERSIGHT LAYER
RED TEAMING
POLICY
INTENT
Everyone’s Freaking OUT!
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POCs Fail �Because:
The how
The what
The Right What:
Digital Context
Data
Models
Integrations
Context
Applications/Tools
01
02�Latent Knowledge
Representation
INDUSTRY EXPERT
Capture tacit → codify into data models, embeddings, training infrastructure
KNOWLEDGE EXTRACTION & STRUCTURING
03�Oversight
CUSTOMIZATION NEEDED
INFRASTRUCTURE
MODEL
SEMANTIC ABSTRACTION
ORCHESTRATION
APPLICATION
The Wrong What:
POCs Fail �Because:
The how
The what
The Right How:
Lessons From Aerospace
Lessons From Aerospace
Systems Engineering
Lessons From Aerospace
A New Approach
Spec Definition
Flight Feedback & Data Analysis
Module & Subsystem Refinement
Launch!
Build Simplest MVP
Closed Loop Engineering
Iterative Engineering Lifecycle
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Confidential | ©2025 Scale Inc.
INTERNATIONAL BASE CAMP
Lessons From Aerospace
Lessons From Aerospace
Problem Definition
User Feedback & Data Analysis
Model & Workflow Refinement
Launch! to User
Build Simplest MVP
Forward Deployed Engineering
Iterative Engineering Lifecycle
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Confidential | ©2025 Scale Inc.
INTERNATIONAL BASE CAMP
The Wrong How:
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Treating AI like normal software
For Successful GenAI POCs:
Thank you!