Awesome AI Engineering

Overview

Awesome AI Engineering provides a comprehensive playbook for building production-grade LLM applications and AI systems. This resource covers the full stack of AI engineering, from model fine-tuning and post-training to system architecture, evaluation, and deployment at scale.

Features

• Architectural Patterns: Design patterns for AI agents and RAG systems
• MCP Integration: Model Context Protocol for agent development
• Post-Training Techniques: SFT, DPO, QLoRA, and other fine-tuning methods
• Data Pipelines: Building and managing training data workflows
• Evaluation Frameworks: Comprehensive model and system evaluation
• System Design: Production architecture for LLM applications
• Domain Adaptation: Techniques for specializing models
• Deployment Strategies: Serving and scaling LLM systems

AI Agent Architecture

Agent Patterns

• ReAct: Reasoning and Acting framework
• Chain-of-Thought: Step-by-step reasoning
• Tree of Thoughts: Exploring multiple reasoning paths
• Reflection: Self-critique and improvement
• Multi-Agent Systems: Collaborative agent architectures

MCP (Model Context Protocol)

• Standardized context management
• Tool integration patterns
• Memory management
• State persistence
• Agent communication protocols

Tool Integration

• Function calling patterns
• Tool selection strategies
• Error handling and retries
• Rate limiting and quotas
• Tool result parsing

RAG (Retrieval-Augmented Generation)

Architecture Patterns

• Basic RAG: Simple retrieval and generation
• Hybrid Search: Combining dense and sparse retrieval
• Multi-Query RAG: Query expansion and reformulation
• Parent-Child Chunking: Hierarchical document structure
• Re-ranking: Improving retrieval relevance

Retrieval Strategies

• Vector search with embeddings
• BM25 keyword search
• Hybrid fusion methods
• Contextual compression
• Query routing

Optimization Techniques

• Chunk size optimization
• Embedding model selection
• Context window management
• Cache strategies
• Latency optimization

Post-Training Recipes

Supervised Fine-Tuning (SFT)

• Dataset preparation and curation
• Training hyperparameters
• Learning rate schedules
• Batch size optimization
• Overfitting prevention

Direct Preference Optimization (DPO)

• Preference data collection
• Reward model training
• Policy optimization
• Alignment techniques
• Safety considerations

QLoRA (Quantized LoRA)

• 4-bit quantization
• LoRA adapter training
• Memory-efficient fine-tuning
• Merge and deployment
• Performance vs. efficiency trade-offs

Domain Adaptation

• Continued pre-training
• Task-specific fine-tuning
• Few-shot adaptation
• Transfer learning strategies
• Multi-task learning

Data Pipelines

Data Collection

• Web scraping strategies
• Synthetic data generation
• Data augmentation
• Active learning
• Human-in-the-loop labeling

Data Processing

• Cleaning and deduplication
• Quality filtering
• Format standardization
• Tokenization strategies
• Dataset versioning

Data Quality

• Quality metrics
• Outlier detection
• Bias detection
• Diversity measurement
• Validation protocols

Evaluation Frameworks

Model Evaluation

• Perplexity and loss metrics
• Task-specific benchmarks
• Human evaluation
• A/B testing
• Safety evaluations

System Evaluation

• End-to-end latency
• Throughput measurement
• Cost per request
• Success rate tracking
• User satisfaction metrics

LLM-as-Judge

• Automated evaluation with LLMs
• Rubric-based assessment
• Pairwise comparison
• Multi-dimensional scoring

System Design

Architecture Patterns

• Microservices for LLM apps
• Event-driven architectures
• Async processing
• Queue management
• State management

Scalability

• Horizontal scaling strategies
• Load balancing
• Caching layers
• Database optimization
• CDN integration

Reliability

• Error handling and retries
• Circuit breakers
• Fallback strategies
• Monitoring and alerting
• Disaster recovery

Deployment and Serving

Model Serving

• vLLM for high-throughput
• TGI (Text Generation Inference)
• Ray Serve for distributed
• Triton Inference Server
• Custom serving solutions

Optimization

• Quantization (INT8, INT4)
• Model pruning
• Knowledge distillation
• Speculative decoding
• Continuous batching

Infrastructure

• GPU selection and optimization
• Kubernetes deployment
• Serverless options
• Multi-region deployment
• Cost optimization

Best Practices

Development

• Start with prompting before fine-tuning
• Use smaller models when possible
• Implement comprehensive logging
• Version everything (models, data, code)
• Build evaluation early

Production

• Monitor model performance continuously
• Implement rate limiting
• Handle failures gracefully
• Collect user feedback
• Plan for model updates

Ethics and Safety

• Content filtering
• Bias mitigation
• Privacy protection
• Toxicity detection
• User consent management

Pricing

Free and open-source resource.