Opinion piece argues that API design and tooling choices by model providers affect LLM reliability, not just the model itself.
The article critiques how APIs constrain input/output, preferring chat templates and limiting prefill, logprobs, and reasoning tokens. It argues that these restrictions affect developer control and reliability. It suggests that more advanced endpoints and access to internal signals could improve reliability, though some practices aim to mitigate risks like prompt injections.
Lead coverage: OReilly AI and ML — Don’t Blame the Model ↗
The article critiques how APIs constrain input/output, preferring chat templates and limiting prefill, logprobs, and reasoning tokens. It argues that these restrictions affect developer control and reliability. It suggests that more advanced endpoints and access to internal signals could improve reliability, though some practices aim to mitigate risks like prompt injections.