Table of contents

Introduction

AI agents are autonomous systems powered by Large Language Models (LLMs) that can understand, reason, and perform tasks. This article explores key components and considerations in building effective AI agent systems.

Why AI Agents Matter

• Automation of complex cognitive tasks
• Enhanced decision-making capabilities
• Scalable personalized interactions
• Cost-effective operations

Current Industry Applications

• Customer Service: Intelligent support systems
• Content Creation: Automated writing and editing
• Research: Information synthesis and analysis
• Development: Code generation and debugging

Key Challenges

• Hallucination management
• Context window limitations
• Cost optimization
• Reliability and consistency

Evaluation

Definition

Evaluation frameworks assess LLM performance across various dimensions including accuracy, reliability, and safety.

Methodology

• Dataset Selection
  • Standard benchmarks (GLUE, SuperGLUE)
  • Domain-specific datasets
  • Synthetic test cases
  • A/B testing scenarios
• Task Definition
  • Classification
  • Generation
  • Question-answering
  • Reasoning
  • RAG quality assessment
• Metrics
  • Accuracy for classification
  • F1 score for balanced evaluation
  • BLEU for translation
  • ROUGE for summarization
  • Custom metrics for specific use cases
  • Cost per successful interaction

Best Practices

• Use diverse evaluation datasets
• Implement user feedback loops
• Maintain human-labeled test sets
• Regular performance monitoring
• Cost-benefit analysis
• Continuous A/B testing

Embedding

Core Concepts

• Convert text to vector database
  • Split the text into tokens(chunks)
    • Choose qualified tokens
    • Remove noisy tokens
    • Ensure semantic meaning
    • Use overlap to store the range of the tokens
  • Use the embedding model to convert the tokens into vectors
  • Store the vectors in the database(use meta data to index)
    • ex, Pinecone, Weaviate, or FAISS, upstash

Embedding Models Comparison

• OpenAI text-embedding-ada-002
  • High quality, but costly
  • 1536 dimensions
• BERT/MPNet based models
  • Open-source alternatives
  • 768-1024 dimensions
• Sentence transformers
  • Optimized for semantic similarity
  • Various dimension options

Chunking Strategies

• Fixed size chunks
  • Pros: Simple implementation
  • Cons: May break semantic units
• Semantic chunking
  • Based on paragraphs/sections
  • Preserves context better
• Overlap techniques
  • 10-20% overlap recommended
  • Helps maintain context
• Metadata preservation
  • Source tracking
  • Timestamp information
  • Category labels

RAG (Retrieval Augmented Generation)

Architecture

• Query Processing
  • Query understanding
  • Query expansion
  • Query optimization
• Retrieval Pipeline
  • Convert query into an embedding
  • Search vector database
  • Filter and rank results
  • Context window optimization
• Response Generation
  • Prompt engineering
  • Context injection
  • Output validation

Implementation Best Practices

• Use namespace to classify data
• Implement reranking strategies
• Handle context window limitations
• Cache frequent queries
• Monitor retrieval quality
• Implement fallback strategies

Common Challenges

• Context relevance
• Information freshness
• Response consistency
• Cost optimization
• Performance tuning

Structured Data

• Reformat the output of the LLM model to structured data
• Schema validation
• Error handling
• Type checking
• Data cleaning

Human in the Loop

Review Process

• Review/Check the output of the LLM model
  • Content accuracy
  • Safety checks
  • Bias detection
  • Quality assurance

Feedback Systems

• Use feedback mechanisms
  • Direct corrections
  • Preference learning
  • Reward modeling
  • User satisfaction metrics

Decision Support

• Support decision making
  • Confidence thresholds
  • Escalation paths
  • Expert review triggers
  • Risk assessment

Optimization

• Training and Fine-tuning optimization
  • Input prompt engineering
  • Context window optimization
  • Few-shot example curation
  • Model performance tracking

Benefits

• Improved accuracy and reliability
• Reduced operational risks
• Accelerated learning cycles
• Enhanced safety and control
• Quality assurance
• Continuous improvement

Memory Management

Types of Memory

• Important-based retention
  • Critical elements
  • Key decisions and outcomes
  • User preferences and settings
  • Established context and background
  • Relevant constraints or requirements

Memory Operations

• Storage strategies
  • Vector storage
  • Key-value pairs
  • Hierarchical organization
• Retrieval methods
  • Semantic search
  • Time-based retrieval
  • Priority-based access
• Pruning mechanisms
  • Importance scoring
  • Time-based expiration
  • Usage frequency

Performance Optimization

Caching Strategies

• Query caching
• Embedding caching
• Result caching
• Cache invalidation

Batch Processing

• Parallel retrieval
• Batch embedding
• Query optimization
• Load balancing

Monitoring

• System metrics
• Response times
• Error rates
• Cost tracking
• Quality metrics