Building Generative AI Applications in Go Gari Singh, Google

Ollama allows running AI models locally, supporting multiple models like Llama, Mistral, and Gemma with a simple API interface

GenKit provides a standardized way to interact with AI models in Go, offering a common interface across different model providers

Langchain Go offers a full port of Python’s Langchain capabilities, allowing Go developers to build AI applications without needing Python

Models generally work through prompts, which can be single-shot (one-time) or conversational, with system and user messages providing context

Temperature settings in models control response variability - lower temperature means more consistent/predictable outputs

Model serving frameworks handle the complexities of loading models and processing requests, abstracting away the technical details

Hardware acceleration (GPUs) is important for running larger models efficiently, with NVIDIA CUDA being the dominant platform

Firebase GenKit provides a way to build AI applications with Go while leveraging Google’s infrastructure

Prompts are essentially the “code” for AI models - proper prompt engineering is crucial for getting desired results

The Go ecosystem for AI is growing, with tools focusing on making models easier to use and integrate into existing applications