Cohere, OpenAI and AI21 Labs Develop Preliminary Best Practices for Large Language Model Deployment

Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models. The available description presents the practices as broadly relevant to organizations that build or operate such models.

The same short materials also list related topics: “Scaling laws for neural language models,” “Scaling laws for reward model overoptimization,” and “A hazard analysis framework for code synthesis large language models.” In general terms, scaling laws are commonly understood as ways of describing how model behavior may change as resources such as parameters, data, or compute are increased. Reward model overoptimization is a concept associated with the risk that a system improves against a proxy reward signal while diverging from the intended outcome. Hazard analysis frameworks are typically relevant when model outputs can affect software systems, including code synthesis, where generated or transformed code may introduce operational or security risks.

The material does not provide further specifics, such as detailed recommendations, evaluation results, or whether the practices have been adopted beyond the named organizations.

Large language models are typically trained to predict text or generate content across broad domains, and deployment can involve many stages, including data collection, model selection, fine-tuning, safety filtering, monitoring, user interface design, and response handling. Because these models are used by diverse organizations, preliminary best-practice materials can serve as a common reference point rather than a detailed compliance checklist. Such practices are often framed around making assumptions explicit, identifying failure modes, and encouraging careful evaluation before and after release.

From a risk-management perspective, the listed topics point to recurring concerns. Scaling-law materials help organizations think about how changes in model size, training data, or computational budget may affect capabilities, reliability, and unforeseen behaviors. Reward-model overoptimization highlights a common alignment problem: when a model or optimizer is tuned against a simplified signal, it may learn to satisfy that signal without satisfying the underlying goal. Hazard-analysis frameworks are useful where model outputs can be executed, integrated into applications, or used to generate code; in those settings, text generation is not merely a display task but can influence software behavior.

The practical value of a preliminary set of best practices is that it can prompt organizations to ask structured questions: What uses are expected, and what uses are discouraged? Who is responsible for reviewing outputs? How will failures be logged, assessed, and mitigated? How will prompts, filters, retrieval systems, and human oversight be combined? These questions do not require any specific tool or architecture, but they can shape safer deployment processes across different environments.

Because the available material is brief, the contribution is best treated as a starting framework rather than a complete standard. More detailed guidance would typically include evaluation procedures, examples of acceptable outputs, incident-response steps, and documentation of model limitations. Even without those specifics, the emphasis on scaling, reward optimization, and hazard analysis signals attention to anticipating behavior, monitoring unintended consequences, and considering downstream effects when language models are used in software systems.

In summary, the contribution is a high-level, preliminary set of deployment practices linked to machine learning safety and systems risk. It connects model development to operational concerns: how models may change as resources grow, how simplified objectives can mislead optimization, and how generated code can create hazards. The absence of detailed recommendations or adoption information means the material is best understood as an invitation to structured thinking, not as a finalized rulebook.