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Modern generative AI is best understood as a component in an application—not a self-contained product or a guarantee of correct answers. For developers, the practical job is to define the task, select a model that fits it, provide the right instructions and context, evaluate real outputs, and design safeguards around the consequences of failure.
What “modern AI” means in a developer’s context
Foundation models are trained on data to learn patterns that can support applications such as content generation. Large language models (LLMs) are foundation models trained on text and often use deep-learning architectures such as Transformers. Some models also handle other modalities, including images, audio, or video; the specific inputs and outputs available depend on the model.
That is a useful working definition for generative AI, not a definition of all AI. Classical machine learning, robotics, and symbolic systems are outside this overview.
A feature built with a generative model is more than the model itself. It includes the application’s input preparation, instructions, any retrieved information or tools, output handling, evaluation, safety measures, and deployment decisions. A model may produce useful code, summaries, or answers and still return inaccurate or unexpected content. The quality of the feature depends on how the whole system is designed and assessed.
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How to choose a model for the task
Start with what the application must do, then compare models against the requirements that matter for that use case. Google Cloud recommends choosing the most affordable model that still meets quality and latency requirements. A larger model in the same family may provide higher-quality responses, but can also increase latency and cost; size alone does not establish suitability. Google Cloud’s generative AI application guidance recommends experimenting and evaluating rather than assuming one model is best.
- Task and modality: Check that the model supports the kind of work and input or output your feature needs.
- Quality: Test representative examples from the intended use case, including difficult cases.
- Latency and cost: Assess whether responses are fast enough and affordable in the application’s actual operating conditions.
- Size and required features: Treat these as selection factors, not as substitutes for task-specific evaluation. Confirm capabilities in the selected model’s current documentation.
When to use prompting, retrieval, or fine-tuning
Prompting, retrieval-augmented generation (RAG), and fine-tuning solve different problems. They are not required steps in a fixed sequence. Establish a prompt baseline, evaluate it, diagnose the failure, and choose a targeted change. OpenAI’s guide to optimizing LLM accuracy frames context optimization and model or behavior optimization as approaches that can be combined when a use case needs both.
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| Approach | Best fit | What to evaluate |
|---|---|---|
| Prompting | Giving the model instructions, constraints, or examples of the desired output. | Whether the prompt reliably produces the required task behavior, format, and quality on representative and held-out examples. |
| RAG | Supplying external information the model may not reliably know, such as domain-specific, proprietary, or changing material. | Whether retrieval finds relevant and sufficiently complete material, and whether the model uses it correctly in its answer. |
| Fine-tuning | Teaching task behavior from examples when outputs need more consistent execution or a task may be performed efficiently with fewer tokens or a smaller model. | Whether the tuned model improves the target behavior on held-out examples without harming other important capabilities. |
Use prompting for instructions and examples
A prompt template can specify the task, constraints, tone, and output format. Few-shot examples can show what a good response looks like. Google’s alignment guidance notes that templates can improve quality and safety, but are less robust than tuning and more exposed to adversarial inputs. Test the prompt on data that was not used to develop it. Google’s model-alignment guidance also cautions that excessive safety tuning can impair other capabilities and that what counts as safe depends on the application.
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RAG retrieves relevant material and adds it to the model’s prompt. It can give the model access to information not reliably present in its learned knowledge, but it also adds a retrieval step that can fail. The system may find irrelevant or incomplete material, or the model may misuse material that retrieval found correctly. Evaluate retrieval quality and the generated answer separately.
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Use fine-tuning for learned task behavior
Fine-tuning continues training from a model checkpoint using examples that represent the desired task or behavior. It may improve task accuracy or efficiency, including cases where similar performance can be achieved with fewer tokens or a smaller model. It is not a replacement for providing changing or proprietary facts at answer time. Keep held-out examples for evaluation and select this approach only when it addresses the diagnosed failure.
How to evaluate a model-powered feature
Evaluation is an iterative engineering activity, not a one-time model-selection score. Define what a good result means for the specific use case, examine representative failures, make a targeted change, and measure again. Accuracy and consistency have to be judged against the application’s requirements; a poor draft that a writer can correct does not carry the same error cost as an incorrect consequential financial decision. OpenAI’s accuracy guidance emphasizes that acceptable error depends on the task.
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- Define the task and failure cost. Specify what a successful output must do and what harm an incorrect or inconsistent result could cause.
- Build representative evaluations. Include ordinary cases and likely edge cases. For prompts, include examples that were not used in prompt development; for fine-tuning, retain held-out examples.
- Inspect failures, not just aggregate results. Determine whether a problem comes from missing or stale context, inconsistent instructions or execution, poor retrieval, or output handling in the application.
- Change the part implicated by the failure. For example, add or update context when needed information is missing, or adjust behavior instructions when format or task execution is inconsistent.
- Measure again and monitor deployment. Confirm that the change improves the intended outcome and watch for failures that appear with real usage.
What safeguards belong around the model
Generative systems can produce inaccurate, biased, offensive, or otherwise unexpected results. Documented limitations include edge cases, hallucinations, bias amplification, variation in language quality, limited domain expertise, and input or output length limits. The right controls depend on the application and on the impact of an error.
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- Assess likely harms for the users and context in which the feature will operate.
- Run safety testing and configure available filters where they suit the use case.
- Solicit feedback and monitor how the system is used after deployment.
- Use human review at critical decision points or where quality control and user impact warrant it.
Filters and grounding can help, but they do not guarantee safe or factual outputs. Google Cloud states that human review can help with responsible-use decisions, specific quality-control requirements, or monitoring generated content. Its Responsible AI guidance and Google AI’s safety and factuality guidance place responsibility on developers to understand limitations, test the system, and account for the application’s context.
Quick Recap
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