The GenAI Platform: Reusable Components & Patterns

The GenAI Platform Concept

Organizations that scale GenAI successfully don't rebuild from scratch for each use case. They invest in reusable components that accelerate every subsequent project.

McKinsey's GenAI research finds that 31% of high performers have adopted a modular component approach—compared to only 11% of others. This is a key differentiator. Meanwhile, many companies get stuck doing one-off pilots because they lack platform thinking.

From One-Off to Scalable

Workplace Scenario: The Support Assistant

Your business unit wants to deploy a GenAI assistant to help customer support agents find answers faster. You're about to start building from scratch when a colleague mentions that the central platform team maintains some reusable assets.

You discover:

• A prompt library with tested customer service prompts (tone, escalation handling, response templates)
• A knowledge base infrastructure already connected to product documentation and FAQs
• An LLM gateway that handles authentication, rate limiting, and cost tracking
• An evaluation framework to measure response quality and hallucination rates

Instead of 2 months of work, you can:

1. Use existing prompts as your starting point
2. Connect to the shared knowledge base (or add your team's content)
3. Route requests through the LLM gateway
4. Measure quality using established evaluation patterns

Result: Proof of concept in 2 weeks. Consistent quality. Visible costs. Built-in guardrails.

This is the power of the GenAI platform.

Key Platform Components

1. Prompt Library

What it is: A centralized repository of tested, versioned prompts that teams can reuse and adapt.

Benefits:

• Don't reinvent prompts for common tasks
• Consistent quality and tone across the organization
• Learn from what works (and what doesn't)
• Version control for prompt iterations

Example Prompts:

What makes a prompt library valuable:

• Clear naming conventions
• Documentation of when to use each prompt
• Version history with change notes
• Performance metrics (if available)

2. Knowledge Base Infrastructure (RAG)

What it is: Shared infrastructure for connecting GenAI to your organization's information—vector stores, embedding pipelines, and retrieval systems.

Benefits:

• Teams don't each build their own RAG pipeline
• Consistent chunking and embedding strategies
• Centralized content updates flow to all applications
• Shared investment in retrieval quality

Components:

Example: Instead of each team figuring out how to embed and search company documents, they call a central retrieval API: "Give me the 5 most relevant chunks for this query from the product-docs collection."

3. LLM Gateway

What it is: A centralized layer that sits between your applications and LLM providers, handling cross-cutting concerns.

Benefits:

• Single place to manage API keys and access
• Cost tracking and budget controls per team/project
• Rate limiting and retry logic
• Easy model switching (swap GPT-5.x for Claude without code changes)
• Consistent logging and audit trails

What it handles:

4. Evaluation Framework

What it is: Tools and processes for measuring GenAI output quality consistently across use cases.

Benefits:

• Know if your prompts are working before production
• Detect quality degradation over time
• Compare different approaches objectively
• Build confidence for stakeholders

Evaluation Approaches:

Example Metrics:

• Hallucination rate (% of responses with unsupported claims)
• Relevance score (does the response address the question?)
• Format compliance (does output match expected structure?)
• Tone alignment (does it match brand voice?)

Matching Exercise: Components and Benefits

Match each platform component to its primary benefit:

How Components Accelerate Delivery

Data Flow Through the GenAI Platform

01User Query
02    ↓
03LLM Gateway (auth, routing, logging)
04    ↓
05Prompt Library (select/customize prompt)
06    ↓
07Knowledge Base (retrieve relevant context)
08    ↓
09LLM API (generate response)
10    ↓
11Evaluation (log quality metrics)
12    ↓
13Response to User

Each layer builds on the ones below. When you start a new project, you're not starting from zero—you're building on a foundation.

Time Savings by Component

Building vs. Buying Platform Components

Not every organization needs to build everything in-house. Consider:

Emerging tools: Platforms like LangChain, LlamaIndex, Weights & Biases, and Humanloop provide building blocks. Cloud providers offer managed vector stores and model endpoints. The "build vs. buy" decision depends on your scale, security requirements, and internal capabilities.

Group Reflection: What Could Be Reused?

Think about a GenAI project your team has built or is building:

Questions to consider:

• Did you develop prompts that work well and could be shared?
• Is there knowledge base content that other teams might need?
• Did you solve an integration challenge others will face?
• What would you have wanted to exist when you started?

The contribution mindset: Champions don't just consume platform components—they contribute back. A successful prompt, a useful retrieval pattern, a quality evaluation rubric—these become organizational assets.

Completion: Component Selection

Scenario: You need to build a GenAI assistant that helps sales reps prepare for customer calls by summarizing account history and suggesting talking points.

Available Enterprise Components:

1. Prompt library – includes summarize_customer_history_v2 and generate_talking_points_v1
2. CRM knowledge connector – syncs Salesforce data to the vector store nightly
3. LLM gateway – provides access to GPT-4 and Claude with per-team cost tracking
4. Evaluation framework – includes relevance scoring and hallucination detection

Quiz: How would you approach this? Which components would you use?

Recommended Approach:

1. Start with the prompt library—adapt existing prompts for your specific sales context
2. Connect to the CRM knowledge connector to pull account history
3. Route through the LLM gateway for consistent access and cost visibility
4. Use the evaluation framework to test output quality before launch

Your Contribution Plan

Exercise: Identify one way you could contribute to your organization's GenAI platform.

Consider:

• A prompt that works well and should be documented
• Knowledge base content your team owns that others need
• An integration pattern that could be generalized
• An evaluation approach that proved useful

Examples:

• "Our customer objection handling prompt should be in the prompt library"
• "We should add our product FAQ content to the shared knowledge base"
• "Our approach to measuring response tone could become a standard metric"

Key Takeaways

• The GenAI platform approach industrializes delivery through reusable components
• Key components: prompt libraries, knowledge base infrastructure, LLM gateways, evaluation frameworks
• 31% of AI high performers use modular components vs. only 11% of others—this is a differentiator
• Champions both consume and contribute to platform assets
• Starting with existing components can reduce time-to-value by 75% or more

Congratulations!

You've completed the Practitioner Track foundation modules. You now understand:

• Strategy: Connecting GenAI to business value (Module 1)
• Prioritization: Assessing readiness and ranking opportunities (Module 2)
• Ethics: Responsible AI principles and practices (Module 3)
• Governance: Documentation, escalation, and oversight (Module 4)
• Operating Models: How to work within centralized, federated, or hybrid structures (Module 5)
• Platforms: Leveraging reusable components to scale (Module 6)

What's Next?

Continue to Data Literacy for GenAI and Prompt Engineering modules, or explore the Trainer Track to learn how to facilitate AI learning for others and build communities of practice.

Your journey as an AI Champion continues!