Generative AI development is a sequence of decisions: define the intended use, choose or build a model, prepare data, adapt and evaluate the model, then integrate it into software. The stages can repeat as teams find problems or change requirements. A team using an existing model may not do its original pretraining at all.
How is generative AI developed?
A foundation model is trained on broad data—generally using self-supervision at scale—and can then be adapted for many downstream tasks, according to Stanford’s Center for Research on Foundation Models (CRFM). That broad reuse can make development more flexible, but weaknesses in a base model can also carry into applications built on it.
The sequence below is a useful map, not a one-way recipe. The work varies by modality and intended use: text, image, audio, and multimodal models do not all use an identical technical pipeline.
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Define the use and constraints
Specify what the system should generate or help users do, who will use it, and what a harmful or incorrect result would mean in that setting. These decisions shape whether a team needs to build a model, adapt an existing one, or integrate a model made elsewhere. They also inform which data, safeguards, and evaluation questions matter.
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Source and prepare data
Data work can include sourcing, selecting, curating, inspecting, cleaning, documenting, and assessing quality. Suitability depends on the intended use and on whether the data can be used for that purpose. Selection choices affect what a model can represent and where it may fail; data is not a neutral input. Stanford CRFM identifies limited transparency about training data and unclear selection principles as concerns in the foundation-model ecosystem.
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Design and train a model—or select one
When building a model, developers choose a model design and training setup, then train it using selected data. Broad training can produce capabilities that are useful across tasks and later adaptation. The specific design and training methods depend on the task and modality. NIST’s July 2024 secure-development profile for generative AI and dual-use foundation models includes model design and training in its development scope.
When starting with an existing foundation model, the team selects a base model rather than repeating its original broad training. It still needs to understand whether that model is suitable for the intended application and how its limitations may affect the result.
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Adapt the model for the task
A pretrained model may be used as-is, guided through prompts, or adapted further. Fine-tuning is one common way to change model behavior using task-relevant examples, but it is not a required step for every application. Stanford CRFM notes that prompting and lightweight alternatives can offer useful accuracy-efficiency trade-offs; the appropriate approach depends on what needs to change, available data, and implementation constraints.
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Evaluate capabilities, limitations, and risks
Tests should reflect the intended use, not just a general-purpose score. Teams may examine task performance, robustness to changed inputs, fairness, efficiency, environmental impact, and relevant safety or security risks. A model benchmark can indicate performance on a defined test, but cannot by itself establish how a complete application will behave for its users.
NIST’s Generative AI evaluation program aims to measure capabilities and limitations across modalities, conduct adversarial evaluation, evolve benchmark datasets, and study how prompting affects credible and misleading content. These are program aims, not a certification that a model is safe.
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Integrate the model into software
In a product, a model is connected to software components, interfaces, data flows, and safeguards. NIST SP 800-218A includes incorporating and integrating models into other software as part of model development. Whether the model is built internally or supplied by another organization, integration is where its behavior meets the application’s actual inputs and user experience.
Should you build from scratch or adapt an existing model?
The choice changes what the development team controls and what work it inherits. The evidence supports these broad trade-offs, but does not establish universal cost or performance figures for either route.
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| Consideration | Build a foundation model | Adapt or integrate an existing model |
|---|---|---|
| Training and data | The team selects the data and training setup for broad model development. | The team starts from a model trained elsewhere; its original training is not part of the team’s work. |
| Control | More direct control over base-model design and training choices. | Choices are constrained by the selected model and the ways it can be accessed or adapted. |
| Task fit | Broad training is intended to support adaptation to many tasks, but does not guarantee a fit for a particular application. | The starting model may already provide useful capabilities; task fit still needs to be assessed. |
| Evaluation burden | The team must evaluate the model it develops and the application that uses it. | The team must assess both the inherited model’s limitations and the behavior of its own application. |
| Inherited limitations | Limitations can arise from the team’s own data and design choices. | Base-model weaknesses may propagate into downstream applications. |
How do prompting and fine-tuning differ?
These are alternative ways to shape a model’s behavior, not steps every developer must perform in sequence.
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| Approach | What changes | Useful when |
|---|---|---|
| Prompting | The model receives instructions or context at use time; its underlying model is not thereby retrained. | The desired behavior can be elicited through instructions and context, and the team wants to avoid changing model weights. |
| Fine-tuning | The model is adapted using additional task-relevant training examples. | The team needs a more persistent task-specific change and has suitable examples and resources to carry out and evaluate adaptation. |
| Lightweight adaptation | A less extensive adaptation approach is used instead of, or alongside, full fine-tuning. | The team is balancing task performance against efficiency and implementation constraints. |
Stanford CRFM describes potential accuracy-efficiency trade-offs for prompting and lightweight alternatives, rather than identifying one universally best approach. A fair comparison should use the same intended task and account for data availability, cost or compute, latency, and the degree of behavioral change required.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why is a model benchmark not enough?
A benchmark score describes performance under the benchmark’s defined conditions. It does not automatically capture robustness to unusual inputs, fairness across relevant groups, resource efficiency, environmental impact, or the behavior of the full software application in its intended context. Evaluation should therefore connect model-level measures to the system’s use, including the ways users and other software components interact with it.
NIST’s AI Risk Management Framework describes testing, evaluation, verification, and validation (TEVV) tasks across the AI lifecycle. This makes evaluation a continuing development concern rather than a single final score. NIST’s GenAI evaluation work likewise includes adversarial evaluation and studies of prompting effects; those activities help examine limitations, but do not amount to a universal safety guarantee.
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Where does model development end?
NIST SP 800-218A, published in July 2024, is a secure-development profile for generative AI and dual-use foundation models. It states that its scope covers AI model development, including data sourcing, design, training, fine-tuning, evaluation, and integration into other software. The profile expressly excludes deployment and operation of AI systems from its scope.
That boundary matters: releasing an integrated product introduces operational responsibilities beyond the profile’s model-development scope. Monitoring use, responding to incidents, and governing the running system belong to the broader AI-system lifecycle; they should not be mistaken for a detailed post-release procedure specified by SP 800-218A.
