Turning a generic LLM into a Ruby-LibGD expert, one correction at a time. A real-world experiment in hallucinations, context, RAG, and why context is not the sa
A developer details their experience adapting a general-purpose large language model to become an expert in Ruby-LibGD. This process involved iterative corrections to address hallucinations and improve context understanding. The experiment highlights the distinction between context and training data for LLMs. AI
IMPACT Demonstrates a method for specializing LLMs for niche domains, potentially improving their utility in specific technical fields.