Building Generative AI Applications in Go (continued)- Gari Singh, Google

RAG (Retrieval Augmented Generation) is one of the most common approaches for augmenting LLMs with custom data without fine-tuning the model

Vector databases and embeddings are key components for implementing RAG - they allow efficient storage and retrieval of relevant context

Go has several frameworks and toolkits for building GenAI applications:

• LangChain Go
• GenKit from Firebase
• Native Google AI APIs
• Ollama for local model deployment

Prompt engineering and providing proper context are critical for getting good results from models - you need to explicitly tell models to use provided context

Vector embeddings help convert unstructured data (text, code, etc.) into a format that can be efficiently searched and retrieved as context for LLMs

Models typically have token limits for both input and output - you need to consider context window size when designing applications

Testing and validation of GenAI applications is challenging since outputs aren’t deterministic - need alternative approaches versus traditional unit testing

For production systems, it’s important to have guardrails and validation to prevent hallucination and ensure responses use provided context

The ecosystem provides plugin architectures that make it easy to swap different models, vector stores, and embeddings implementations

Focus on orchestration and data processing pipelines rather than trying to build separate tiers for each component