Turning a Generic LLM Into a Ruby-LibGD Expert (One Correction at a Time)

A developer shares observations from a day spent trying to get a local LLM to help with Ruby-LibGD, a niche Ruby binding for the GD Graphics Library. The model repeatedly hallucinated plausible-but-wrong API methods drawn from similar libraries (PHP's GD, older bindings), and despite repeated corrections, kept reverting to its training data patterns. The key insight: context provided during a conversation is not the same as training — the model continuously negotiates between pretraining weights and in-context information. Over time, with enough examples, corrections, and documentation, the model gradually converged toward correct API usage within the session, acting as a 'temporary expert' without any permanent weight changes. The takeaway for developers working on niche or obscure libraries is that they must act as teachers, constructing a temporary knowledge layer around the model through repeated reinforcement.