Lee Harrold

GenAI Capability Combinations Guide

Common Solution Patterns

Enhanced Search & Retrieval

Core Components:
Text Embeddings + RAG + Semantic Search + Ranking Models

What it enables:

• More accurate and contextual search results
• Better handling of complex queries
• Improved content relevance
• Factual grounding for responses

Example Flow:

1. Text embeddings create searchable vectors
2. Semantic search finds relevant documents
3. Ranking models prioritize results
4. RAG incorporates selected content into LLM response

Intelligent Content Generation

Core Components:
RAG + Content Control + Self-Reflection + LLM-as-Judge

What it enables:

• Factually accurate content creation
• Style-consistent outputs
• Quality-controlled generation
• Iterative improvement

Example Flow:

1. RAG provides factual context
2. Content controls manage style/tone
3. Self-reflection checks output quality
4. LLM-as-Judge validates final result

Code Assistant Pipeline

Core Components:
Code Generation + Function Calling + Structured Output + Self-Reflection

What it enables:

• Reliable code generation
• API integration
• Documentation generation
• Code quality checks

Example Flow:

1. Code generation creates initial code
2. Function calling handles API integration
3. Structured output ensures proper formatting
4. Self-reflection validates code quality

Multimodal Processing

Core Components:
Vision Language Models + TTS + STT + Image Generation

What it enables:

• Rich media understanding
• Accessible content creation
• Cross-modal translation
• Interactive experiences

Example Flow:

1. Vision models process visual input
2. LLMs generate textual descriptions
3. TTS converts text to speech
4. Image generation creates visual responses

Automated Workflow Agent

Core Components:
LLMs as Agents + Prompt Chaining + Function Calling + Zero-shot Classification

What it enables:

• Complex task automation
• Dynamic workflow handling
• Flexible task categorization
• System integration

Example Flow:

1. Zero-shot classification categorizes tasks
2. Prompt chaining breaks down complex tasks
3. Agents coordinate execution
4. Function calling integrates with systems

Learning & Adaptation

Core Components:
Fine-tuning + Few-Shot Learning + Knowledge Distillation + Chain-of-Thought

What it enables:

• Domain adaptation
• Efficient learning
• Improved reasoning
• Deployment optimization

Example Flow:

1. Fine-tuning adapts to domain
2. Few-shot learning handles new cases
3. Chain-of-thought improves reasoning
4. Knowledge distillation optimizes for deployment

Advanced Combinations

Content Moderation System

• Vision Language Models + Zero-shot Classification + LLM-as-Judge
• Enables multi-modal content understanding and evaluation

Document Processing Pipeline

• Text Summarization + RAG + Structured Output + Semantic Search
• Creates searchable, structured knowledge bases

Interactive Assistant

• STT + Function Calling + TTS + Self-Reflection
• Enables voice-based interaction with systems

Development Environment

• Code Generation + Fine-tuning + Ranking Models + Chain-of-Thought
• Creates context-aware coding assistance

Content Creation Suite

• Image Generation + Vision Language Models + Content Control + LLM-as-Judge
• Enables controlled multi-modal content creation

Optimization Considerations

Performance Enhancement Pairs

• Model Optimization + Knowledge Distillation
• RAG + Semantic Search
• Fine-tuning + Few-Shot Learning

Quality Control Pairs

• Self-Reflection + LLM-as-Judge
• Chain-of-Thought + Structured Output
• Content Control + Ranking Models

Scalability Pairs

• Text Embeddings + Semantic Search
• Prompt Chaining + Function Calling
• Model Optimization + Knowledge Distillation

Anti-Patterns to Avoid

1. Overcomplicated Chains

   • Avoid: Excessive prompt chaining without clear purpose
   • Instead: Use focused combinations with clear handoffs

2. Redundant Validation

   • Avoid: Multiple layers of LLM-as-Judge and Self-Reflection
   • Instead: Choose most appropriate validation method

3. Inefficient Processing

   • Avoid: Running heavy models when lighter ones suffice
   • Instead: Use Model Optimization and Knowledge Distillation

4. Memory Intensive Combinations

   • Avoid: Large RAG + Complex Prompt Chains
   • Instead: Use efficient retrieval and processing strategies